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How To Beat Traffic Mathematically

Traffic: the commuter’s bane. It plagues major city drivers around the globe and shows no sign of letting up.1 In fact, the average U.S. commuter spends about 100 hours a year driving just to work – 20 hours more than a typical year’s supply of vacation.2 This personal “daily grind” uses more than 15,000 miles and 1,000 gallons of gas every year, which might not be so bad if much of it wasn’t waste: 1.6 million hours and 8 million gallons of gas are wasted every day in traffic jams across the nation. Traffic even affects your health, raising blood pressure, increasing stress, and producing more Type-A personalities.3

Of course, some places are much worse than others. New York tops the list, with Chicago, Newark and Riverside following, albeit at a distance. L.A. comes in at #6 and Houston, where I reside and commute, is #15.4 Other cities, such as Nashville, TN and Kansas City, MO, show up much further down the list, but something tells me that even commuters in those relative traffic havens dedicate significant effort and conversation to ‘beating traffic.’

Resources are sometimes available to help in this quest. Houston Transtar provides up to the minute traffic information for all major Houston highways.5 Average traveling speed, construction and accident information are all available at the click of the mouse, but how to avoid the perpetual web of red during the morning and evening rush hours is nowhere to be found. Obvious answers such as public transportation and carpooling are legitimate, but trends show that Americans are meeting the increase in traffic by using such transportation methods less, not more.6 Also, if the online traffic-reporting graphic warns of potential issues, there is no indication of how long they might persist, leaving the traffic conscientious commuter right where he started: guessing.

Tired of the typically inefficient and contradictory workplace chatter on the subject and feeling the pull of a slight worksheet obsession, I set out to statistically analyze my commute in order to determine how I might minimize my time behind the wheel. If there was a way to figure out how to give myself an advantage over the almost 900,000 other Houstonian workers out there (who average a 26.1 minute commute),7 math and a smidgeon of obsessive compulsive disorder had to be essential ingredients. At the very least, I would be able to ascertain just how much of my commute time was up to me – and how much depended on a “higher power” (e.g., weather, school districts, wrecks, etc.).

Gathering Data

From March of 2005 to March of 2006, I recorded my departure and arrival times both to and from work, along with whether school was in or out.8 Other factors, although most likely important, were excluded to keep the scope of the experiment narrow and measurable.

Driving Data

Every morning, I took note of the time on my car clock as I pulled out of my driveway at the Riata Ranch subdivision of northwest Houston9 and then again as I pulled into the parking garage at my office building close to the north-bound frontage road of Sam Houston Pky and Clay Rd.10 In the evening, I followed the same process in reverse. The morning route 11 and evening route12 differed slightly in length, but data was only recorded when the planned course was followed, allowing for only slight variations.13

School District & Government Data

Being suspicious of the influence of the school session, I collected official 2004-2005 and 2005-2006 calendar data from Cypress Fairbanks Independent School District,14 which covers almost all of my commute route,15 and took note of all full student holidays (i.e., teacher in-service days, but not student early release days).16 I also collected official 2005 and 2006 government holiday information from the city of Houston17 and the US Federal Government,18 but this proved next to useless as I only commuted to work on one city and two federal government holidays.

Analysis

To set up the gathered information, I first organized the variables into inputs and outputs as shown in Table 1.

Table 1: Input and Output Variables

4_article_50_thumb_variables

To determine which variables had a statistically significant effect on my commute times, I ran one-way ANOVAs19 on the discrete variables and plotted smoothed graphs of means for the continuous variables.20

Morning Commute ANOVAs

Day of the Work Week

The one-way ANOVA of the morning commute duration versus the day of work week (y1 vs. x1) showed a statistically significant effect.21 The table in the ANOVA output22 and the boxplot below confirm that this effect comes on Fridays, on which there is a significantly shorter commute time:

Source DF SS MS F P
Day of Week 4 544.4 136.1 3.87 0.005
Error 202 7103.2 35.2
Total 206 7647.6
S = 5.930 R-Sq = 7.12% R-Sq(adj) = 5.28%


Individual 95% CIs For Mean Based on Pooled StDev

Level N Mean StDev ---+---------+---------+---------+----
1 43 22.209 5.726                  (-----*-----)
2 44 22.886 5.891                    (-----*------)
3 47 23.447 6.382                       (-----*-----)
4 39 22.462 7.014                   (-----*------)
5 34 18.559 3.855   (-----*-----)
                   ---+---------+---------+---------+----
                    17.5      20.0      22.5      25.0
Pooled StDev = 5.930
4_article_50_thumb_boxmorningdayofweek

Figure 1. A boxplot of the morning commute time versus the day of the work week.

Week of the Month

The results from an ANOVA of the week of the month versus the morning commute duration (y1 vs. x2) showed no statistically significant impact, although week 5 has the highest average commute time:

Source DF SS MS F P
Week of Month 4 226.5 56.6 1.54 0.192
Error 202 7421.1 36.7
Total 206 7647.6
S = 6.061 R-Sq = 2.96% R-Sq(adj) = 1.04%


Individual 95% CIs For Mean Based on Pooled StDev

Level N Mean StDev --+---------+---------+---------+----
1 52 22.673 7.040          (----*---)
2 44 21.636 4.760      (---*------)
3 51 21.706 6.090     (----*----)
4 42 21.048 5.441   (----*-----)
5 18 24.944 7.075                   (----*----)
                   --+---------+---------+---------+----
                   20.0      22.5      25.0      27.5
Pooled StDev = 6.061
4_article_50_thumb_boxmorningweekofmonth

Figure 2. A boxplot of the morning commute time versus the week of the month.

Month of the Year

The month of the year versus morning commute time (y1 vs. x3) ANOVA results showed even less of an effect:

Source DF SS MS F P
Month of Year 11 496.5 45.1 1.23 0.269
Error 195 7151.1 36.7
Total 206 7647.6
S = 6.056 R-Sq = 6.49% R-Sq(adj) = 1.22%


Individual 95% CIs For Mean Based on Pooled StDev

Level N Mean StDev -----+---------+---------+---------+--
1 21 22.476 5.793                (------*------)
2 8 22.875 4.121              (----------*----------)
3 19 24.053 7.764                    (------*------)
4 19 22.737 4.053                 (------*------)
5 19 23.842 5.650                   (------*-------)
6 18 21.722 5.278            (------*-------)
7 19 18.947 4.441    (------*------)
8 23 20.652 5.556         (-----*-----)
9 17 21.824 7.502            (-------*------)
10 17 21.353 4.182          (------*-------)
11 16 24.250 9.774                   (-------*-------)
12 11 20.545 5.336     (--------*--------)
                   -----+---------+---------+---------+--
                      18.0      21.0      24.0      27.0
Pooled StDev = 6.056
4_article_50_thumb_boxmorningmonthofyear

Figure 3. A boxplot of the morning commute time versus the month of the year.

Cypress-Fairbanks ISD

Whether or not the local school district was in session proved to be the greatest measured variable in explaining the morning commute time variation (y1 vs. x6):

Source DF SS MS F P
CyFair 1 774.0 774.0 23.08 0.000
Error 205 6873.6 33.5
Total 206 7647.6
S = 5.791 R-Sq = 10.12% R-Sq(adj) = 9.68%


Individual 95% CIs For Mean Based on Pooled StDev

Level N Mean StDev --+---------+---------+---------+----
0 63 19.159 4.646    (-----*-----)
1 144 23.361 6.222                        (---*---)
                   --+---------+---------+---------+----
                   18.0      20.0      22.0      24.0
Pooled StDev = 5.791
4_article_50_thumb_boxmorningschool

Figure 4. A boxplot of the morning commute time versus Cypress-Fairbanks ISD Session.

Evening Commute ANOVAs

Day of the Work Week

While the day of the week proved to have a significant impact on the morning commute, the evening commute showed no such relationship (y2 vs. x1):

Source DF SS MS F P
Day of Week 4 68.5 17.1 0.82 0.516
Error 158 3312.1 21.0
Total 162 3380.7
S = 4.579 R-Sq = 2.03% R-Sq(adj) = 0.00%


Individual 95% CIs For Mean Based on Pooled StDev

Level N Mean StDev --+---------+---------+---------+---
1 40 22.125 4.333                (------*------)
2 40 21.275 5.002         (------*------)
3 34 21.706 5.190          (-------*------)
4 33 20.697 4.149       (------*-------)
5 16 22.875 3.304               (----------*----------)
                   --+---------+---------+---------+---
                   19.2      20.8      22.4      24.0
Pooled StDev = 4.579
4_article_50_thumb_boxeveningdayofweek

Figure 5. A boxplot of the evening commute time versus the day of the work week.

Week of the Month

Again, the week of the month did not explain the commute time variation (y2 vs. x2):

Source DF SS MS F P
Week of Month 4 86.4 21.6 1.04 0.390
Error 158 3294.2 20.8
Total 162 3380.7
S = 4.566 R-Sq = 2.56% R-Sq(adj) = 0.09%


Individual 95% CIs For Mean Based on Pooled StDev

Level N Mean StDev --+-------+-------+-------+-----
1 34 21.176 4.496   (-----*-----)
2 39 20.769 4.782 (-----*-----)
3 42 21.857 4.176     (-----*-----)
4 35 22.000 4.583      (-----*-----)
5 13 23.462 5.238         (--------*---------)
                   --+-------+-------+-------+-----
                   20.0    22.0    24.0    26.0
Pooled StDev = 4.566
4_article_50_thumb_boxeveningweekofmonth

Figure 6. A boxplot of the evening commute time versus the week of the month.

Month of the Year

Another change from the morning results, the month of the year proved to have a significant effect, with February, April and November showing the longest evening commute times (y2 vs. x3):

Source DF SS MS F P
Month of Year 11 541.2 49.2 2.62 0.004
Error 151 2839.4 18.8
Total 162 3380.7
S = 4.336 R-Sq = 16.01% R-Sq(adj) = 9.89%


Individual 95% CIs For Mean Based on Pooled StDev

Level N Mean StDev ---+---------+---------+---------+----
1 15 21.400 3.418          (-----*------)
2 9 24.222 3.833                   (-------*------)
3 17 20.529 3.319        (----*-----)
4 10 23.700 6.325                 (------*------)
5 14 20.143 3.416      (-----*------)
6 14 21.357 4.584          (-----*------)
7 14 19.143 4.400   (-----*------)
8 21 21.905 5.078             (----*----)
9 14 20.929 4.811         (-----*------)
10 11 20.091 3.590    (------*------)
11 16 25.625 4.731                       (-----*------)
12 8 20.625 3.021      (-------*-------)
                   ---+---------+---------+---------+----
                    18.0      21.0      24.0      27.0
Pooled StDev = 4.336
4_article_50_thumb_boxeveningmonthofyear

Figure 7. A boxplot of the evening commute time versus the month of the year.

Cypress-Fairbanks ISD

The school session again showed signification influence, but it was not as strong in the evening as in the morning (y2 vs. x6):

Source DF SS MS F P
CyFair 1 106.2 106.2 5.22 0.024
Error 161 3274.4 20.3
Total 162 3380.7
S = 4.510 R-Sq = 3.14% R-Sq(adj) = 2.54%


Individual 95% CIs For Mean Based on Pooled StDev

Level N Mean StDev --------+---------+---------+---------+--
0 50 20.400 4.677    (---------*---------)
1 113 22.150 4.434                        (------*-----)
                   --------+---------+---------+---------+--
                         20.0      21.0      22.0      23.0
Pooled StDev = 4.510
4_article_50_thumb_boxeveningschool

Figure 8. A boxplot of the evening commute time versus the Cypress-Fairbanks ISD Session.

Departure Time Analysis

For the continuous variable of departure time, I plotted smoothed curves of the mean commute time at each minute.

The morning departure time plot shows relatively long commute times until about 7:40AM, at which time a gradual decrease starts that continues in an overall linear fashion for the next hour. After 8:40AM, traffic appears to have only minimal impact. (y1 vs. x4):

4_article_50_thumb_morningdeparture

Figure 9. A smoothed plot of the mean of the recorded morning commute durations versus the home departure time.

The evening departure time plot shows a peak commute time at about 5:10PM, tapering off linearly through the next two or so hours. Departure times prior to 5:00PM showed erratic results, but it is obvious that traffic played a decreasing role in evening commute time duration moving back through 4:00PM, before which it’s influence is noticeable, but slight. (y2 vs. x5):

4_article_50_thumb_eveningdeparture

Figure 10. A smoothed plot of the mean of the recorded evening commute durations versus the work departure time.


4_article_50_thumb_eveningdeparture2

Figure 11. A close-up of Figure 10.

I usually leave home at 8:00AM and work at 5:30PM, but a 30 minute delay of each looks like it would shave five minutes off the morning commute and about 2.5 minutes off the evening. Additional half-hour delays bring 2.5 minutes of commute time savings in the evening, but little to no savings in the morning. Slightly earlier departure times appear to result in commute time increases for both trips. Moving back past 4:30 in the evening brings slight improvement in the evening commute, but savings in the morning would most likely require leaving before 6:30AM.

Conclusions

Given the above data and analysis, what can be done to improve my commute times? Changing my morning or evening departure time looks promising. The best bet appears to be moving my schedule out a half-hour to 8:30AM and 6:00PM, bringing significant savings (about 7.5 minutes of commute time per day) without getting too far from normal business hours. Spread out over 50 work weeks, that results in a total savings of over 30 hours a year – the equivalent of about a 38% boost to my existing 80 hours of vacation.

Departure time isn’t the say-all, however, and making this shift won’t always result in a smooth and fast commute. The day of the week in the morning and the month of the year in the evening both have significant impacts, and whether or not school is in session affects both. I could possibly squeeze out a few more minutes of savings by scheduling my vacation days to align with the potentially longest commutes (e.g., non-Friday school days in the months of November, February and April), but the data shows significant variation up and above that described by the measured variables – much likely due to factors outside of the control of the commuter (e.g., weather, wrecks, breakdowns, response to traffic predictions, etc.).23

The commuter may have more control than it appears, however. Adjusting your commute times and rearranging your vacation schedule will probably help in the meantime, but getting cars off the road is the only sure solution – one that is within commuters’ sphere of influence.24 It might require punching your “free reign” in the gut, but getting involved in your community by writing your Congressperson or attending city council meetings in promotion/defense of improved mass transit could be the most effective way to “curb” your drive times in the long run.25

Notes

1 “Beating Traffic.” Mathematical Moments. American Mathematical Society. 2005. Accessed April 2006 from http://www.ams.org/ams/mm31-traffic.pdf. According to the publication, “In the last 30 years while the number of vehicle-miles traveled has more than doubled, physical road space has increased only six percent.”

2 “Americans Spend More Than 100 Hours Commuting to Work Each Year, Census Bureau Reports.” US Census Press Release_. March 20, 2005. Accessed April 2006 from http://www.census.gov/Press-Release/www/releases/archives/ american_community_survey_acs/004489.htmlacs/004489.html.

3 “Understanding Traffic.” Discovery Channel Features. January 30, 2006. Accessed April 2006 from http://www.odeo.com/audio/674920/view.

4 “Average Travel Time to Work of Workers 16 Years and Over Who Did Not Work at Home.” U.S. Census Bureau: American Community Survey 2003. Accessed April 2006 from http://www.census.gov/acs/www/Products /Ranking/2003/pdf/R04T160.pdf.

5 Houston Real-Time Traffic Map. HoustonTranstar.org. Accessed April 2006 from http://traffic.houstontranstar.org/layers/.

6 Reschovsky, Clara. “Journey to Work 2000.” US Census Bureau. Accessed April 2006 from http://www.census.gov/prod /2004pubs/c2kbr-33.pdf. According to Table 1: Means of Transportation to Work: 1990 and 2000, 2.5% more Americans drove to work alone in 2000 when compared with ten years earlier. All public transportation used saw at least a minor decline.

7 “Houston city, Texas: Selected Economic Characteristics: 2004.” U.S. Census Bureau: American Fact Finder_. Accessed April 2006 from "http://factfinder.census.gov/servlet/ADPTable?bm= y&-geo_id=16000US4835000&-qr_name=ACS_2004ESTG00 DP3&-ds_name=ACS_2004_EST_G00_&-lang=en&-sse=on":http://factfinder.census.gov/servlet/ADPTable?bm=y&-geo_id=16000US4835000&-qr_name=ACS_2004_EST_G00_DP3&-ds_name=ACS_2004_ESTG00&-lang=en&-sse=on.

8 My data can be viewed in an online Google Spreadsheet: http://spreadsheets.google.com/ccc?key=pC3mpnNSv5yLTk6BTrT5AGA

9 Google Local – Cypress N Houston Rd & Riata Ranch Blvd, Houston, TX 77095. Google Maps. Accessed April 2006 from http://maps.google.com/maps?f=q&hl=en&geocode=&q=Riata+Ranch+Blvd+%26+Cypress+N+Houston+Blvd,Houston,TX+77095&sll=29.831994,-95.564298&sspn=0.01035,0.01796&g=Riata+Ranch+Blvd+%26+Cypress+N+Houston+Blvd,Houston,TX+77095&ie=UTF8&t=h&z=16&iwloc=addr. My exact home address is withheld purposely.

10 Google Local – W Sam Houston Pky N & Clay Rd, Houston, TX 77041. Google Maps. Accessed April 2006 from http://maps.google.com/maps?f=q&hl=en&geocode=&q=Clay+Rd+%26+West+Sam+Houston+Pkwy+N,Houston,TX+77041&sll=29.89711,-95.689141&sspn=0.010343,0.01796&g=Clay+Rd+%26+West+Sam+Houston+Pkwy+N,Houston,TX+77041&ie=UTF8&t=h&z=16&iwloc=addr. Again, the exact details of my office location are purposely omitted.

11 My 12.7 mile route to work consists of the following:
1. Proceed .1 miles from home to Riata Ranch Blvd & Cypress N Houston Rd.
2. Proceed west .2 miles on Cypress N Houston Rd.
3. Turn right on Barker Cypress Rd. Proceed .8 miles.
4. Turn right on US-290 E. Proceed 1 mile.
5. Take US-290 ramp. Proceed 6.7 miles.
6. Take Frontage Road Exit to Beltway 8 / FM-529 / Senate Ave. Proceed .7 miles. (I exit here instead of taking the shorter – and most likely faster – freeway to avoid the toll. Yes, I’m cheap and I like spreadsheets.)
7. Turn right on Senate Ave. Proceed 3.1 miles to Clay Rd.
8. Proceed .1 miles to office.

12 My 13.0 mile route home consists of the following:
1. From the office, proceed north on Sam Houston Parkway frontage road for 3.1 miles.
2. Turn left on US-290 frontage road. Proceed 1.0 mile.
3. Take US-290 ramp. Proceed 6.8 miles.
4. Take Barker Cypress Rd Exit. Proceed .9 miles, veering right at split.
5. Turn left on Barker Cypress Rd. Proceed .9 miles.
6. Turn left on Cypress N Houston Rd. Proceed .2 miles to Riata Ranch Blvd.
7. Proceed .1 miles to home.

13 I occasionally took two variations, one on the way to work and one on the way home. In the morning, I sometimes drove around the south side of the fast food restaurants on the southwest bound frontage road of US-290 to avoid the backup at the light at Senate Ave. In the evening, heading north on Senate Ave, I occasionally continued straight under US-290 to avoid the backup in the left-hand turn lanes. Although the road is not shown on the map, the first left after crossing the US-290 frontage road proceeds about .2 miles, then makes a left turn and dead-ends back into the frontage road. A detail of the US-290 and Senate Ave intersection, which contains both variations, is available from Google Maps: http://maps.google.com/maps?f=q&hl=en&q=US-290 W%26+Senate+Ave,Houston,TX+77040&ll=29.877 341,-95.564607&spn=0.006549,0.013561&t=h&om=1

14 Cypress-Fairbanks ISD Home Page. CFISD.net. Accessed April 2006 from http://www.cfisd.net/.

15 Harris County Appraisal District: Index Map: By School District. HCAD: I-Map Publication Service. Accessed April 2006 from http://www.hcad.org/maps/default.asp.

16 As an interesting aside, information was also gathered for surrounding school districts:
* Houston (http://www.houstonisd.org)
* Katy (http://www.katyisd.org)
* Klein (http://www.kleinisd.net)
* Spring Branch (http://www.springbranchisd.com)
* Tomball (http://www.tomballisd.net)
* Waller (http://www.waller.isd.esc4.net)

Analysis indicated that these schedules had no statistically significant impact on my commute, confirming that the effect of the school district schedule is limited to within its own boundaries.

17 “Official City Holidays.” HoustonTX.gov. 2006. Accessed April 2006 from http://www.houstontx.gov/abouthouston/cityholidays.html. 2005 city holidays confirmed via Mrs. Wilkerson of Houston City’s 3-1-1 Helpline, accessible per: “Contact Us.” HoustonTX.gov. 2006. Accessed April 2006 from http://www.houstontx.gov/contactus/index.html.

18 “2005 Federal Holidays.” OPM.gov. Accessed April 2006 from http://www.opm.gov/Fedhol/2005.asp. & 2006 Federal Holidays. OPM.gov. Accessed April 2006 from http://www.opm.gov/Fedhol/2006.asp.

19ANOVA” stands for ANalysis Of VAriance. For more details on ANOVAs and how/when they are used: “Chapter 12: Introduction to ANOVA.” HyperStat Online Textbook_. Accessed April 2006 from http://davidmlane.com/hyperstat/intro_ANOVA.htmlANOVA.html.

20 Discrete variables are those whose values are represented in a limited set. For example, the “day of the work week” variable consists of five values (“Monday” through “Friday”) and a one-way ANOVA analyzes each to determine if it has a significant impact on the result variation. On the other hand, the “departure time” variable is practically continuous, with as many “categories” as there are minutes, and doesn’t lend itself well to ANOVA analysis.

21 For each of the ANOVA analyses, the significance level (α) is .05 and the null hypothesis (H0) is that the input variable has no statistically significant influence on the output. When the Pvalue < α, H0 is thrown out. For example, in the case of the day of the work week vs. the morning commute, the Pvalue is .005, which is less than .05. Thus, it is statistically improbable that the results could have occurred at random and, therefore, the day of the week is shown to exert a significant effect on the morning commute duration.

22 I used Minitab to run the ANOVAs. The top table of the output lists the output variable (Source), the degrees of freedom (DF), the sum of the squares (SS), the mean of the squares (MS), the Fvalue (F) and the Pvalue (P). The lower table lists the input variables (Level), the number of inputs for each (N), the mean of the inputs (Mean), the standard deviation of the inputs (StDev), and then these mean values graphed with a 95% confidence interval (CI) based on the pooled standard deviation. For more information on interpreting the output of one-way ANOVAs: “How to Read the Output From One Way Analysis of Variance.” Jerry Dallal’s Tufts Home Page. Accessed April 2006 from http://www.tufts.edu/~gdallal/aov1out.htm.

23 Some have even suggested chaos theory and driver psychology as ways to best model traffic behavior. More information on chaos theory and traffic: “Chaos and your everyday Traffic Jam.” FailedSuccess.com_. Accessed April 2006 from http://www.failedsuccess.com/index.php?/ weblog/comments/traffic_jam_causes/causes/. More information on driver psychology: Groegera, J. A. and Rothengatter, J. A. “Traffic psychology and behaviour.” Transportation Research Part F: Traffic Psychology and Behaviour. Volume 1, Issue 1, August 1998, Pages 1-9. Accessed April 2006 from http://dx.doi.org/10.1016/S1369-8478(98)00007-2.

24 “Understanding Traffic.” Discovery Channel Features. January 30, 2006. Accessed April 2006 from http://www.odeo.com/audio/674920/view. Every subway train takes 1,000 cars off the road. Every bus, 40 cars.

25 “Critical Relief for Traffic Congestion.” PublicTransportation.org. Accessed April 2006 from http://www.publictransportation.org/pdf/reports/congestion.pdf. Public transportation stands to improve commute times more than departure time adjustment. “The Benefits of Public Transportation: An Overview.” PublicTransportation.org_. Accessed April 2006 from http://www.publictransportation.org/reports/asp/pub_benefits.aspbenefits.asp. Public transportation brings unparalleled reliability and consistency.

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I loved this article. The analysis was absolutely cool, and as a Physics geek, I am kind of embarrassed that I don’t remember hearing about ANOVA analysis until I read the article and looked it up. This article demonstrates profound, nerdy deep thinking about the world around us and doing something within our power about our problems rather than simply impotently pouting about them as less nerdy people are apt. Here’s a guy who put some profound thought into how he can beat the system, and if you read this, beat it he does.

Obviously, time is very important to Brandon. He evidently values using his time more constructively than sitting in a box with wheels sniffing other people’s fumes. One other aspect I’d like to see updated on this is the same kind of analysis for fuel consumption. This will change seasonally due to temperature and other factors, but the shorter times also reflect less wasted fuel on the trip. Obviously, the longer your engine is running, the more fuel you burn, even at an idle. Add to that the typical bumper to bumper nudging forward, and you are sucking a LOT more fuel than simply sitting there at an idle because you are constantly accelerating, and burning up your brakes. Brandon’s maintenance costs and wear and tear on his car are going to drop too as a result of his traffic dodging. Then again, the effect might not be as profound if he is driving a hybrid (follow the link, trust me! ;-) ).

I’ll have to look for some links on other aspects of traffic you can talk to your congressperson about. One is intelligent traffic management systems, that monitor traffic levels and adjust traffic lights to keep large masses of cars moving. Ever pull up to a light and have it turn red on you when there is no cross traffic? Lights should at least be timed so you don’t have to stop at every light.

Construction is also driving me really nuts right now. Construction management in this country needs to get a lot better. I went to visit some friends in Florida over Thanksgiving back in 2000, and on the way back through Louisiana, they had 10 lanes merging down into one lane. The worst part of it was that on many of the lanes there was no work being done on them and they were just blocked off with cones for no apparent reason. Everything between the 90/94/39 junction just north of Madison and Rockford, IL really sucks really badly too, but they don’t have construction as an excuse—it just sucks year round. Construction as we handle it now is very dangerous and wasteful because of all the congestion and resulting stop-and-go traffic and driver fatigue. Then people get out the other end and try to make up for it or have completely lost their patience and get in more accidents, causing more congestion. Don’t even get me started on rubber necking…

0 Votes  - +
Adding up minutes by Anonymous

I enjoyed the meticulous analysis and careful calculations, however I can’t help think of the television commercial with one guy explaining his crazy time saving gimmick to his not-so-interested-friend in an elevator at an office building and at the end he calculates the total time, "that’s fifteen minutes a day, times 5 days, times three years equals a full week of extra time, and I’m thinking Hawaii!!"

"What?"

-Doug

0 Votes  - +
? by milhous

Ok, so I understand the gist of your little experiment. I understand too that you want to keep your variables down. What really throws everything off for me however (aside the fact that all the variables that truly exist get into chaos theory the lack of discussion about traffic lights. There are many types of traffic lights these days that throw in all sorts of "time" type variables. For example, the light by my house on a given day may be green starting at 8AM on that day, and may stay green for 30 seconds. On another given day at 8AM, the light may be red and stay red for 2 minutes and 30 seconds. This slight difference in the grand scheme of my commute can have huge implications on my vehicle standing in the bigger traffic picture.
For example as I am driving, I conceptualize my car as an air bubble amongst many air bubbles in varying viscous fluids in a clear hose. As this air bubble flows at a certain rate, that rate is hindered by differing viscosities (i.e. road sizes, exchanges, light intersections) and by other bubbles (i.e. traffic). When conceptualizing my flow rate this way, I begin to see the different variables that I think play an important role in the chaotic theory involved with commuting.
Brandon hit on some good variables that I never would have thought of, such as schools and governmental data, and kept his experiment relatively simple to the amount of data he was able to collect, but I think that there is a lot more math to be used in this experiment.

I found this article very interesting, and I especially liked the focus on the school variable, however I had a few thoughts on this all.

First, I would like to know to what Brandon attributes the longer commute times in the evening in the months of February, April, and November? I would have guessed it was the combination of darkness and possibly icy conditions, but April doesn’t fit into that mold.

Second, the way I see it there are a LOT of confounding variables in this study. Brandon does say that a lot of the fluctuation in commuting time is probably due to uncontrollable (and unmeasured) variables, such as traffic accidents and weather conditions. But if that is the case, why do the study? I think that these confounding variables probably had more of an effect on the findings than the variables he measured, considering that a bad accident or icy road conditions can lead to a heavy delay in commuting time no matter what day of the week it is in the morning, or no matter what month of the year it is in the evening. Since these confounding variables probably had so much of an effect, I wonder why he would even bother to do this study, since he probably cannot conclude much from it without exploring these other outlets.

Third, I would like to know what the effect size of the significant findings were. We know that there were a couple of significant interactions with the ANOVAs, but we do not know how big these significant effects were. If the effect size is small (partial eta squared equal to less than 0.2) then it is possible that a Type I error was committed (i.e., a false positive, or finding significance when there really was none). This is my suspicion, considering the strong influence those other unmeasured variables that I wrote about above probably had.

All in all, though, I enjoyed reading this article, as it did get me to think about possible ways to reduce my commuting time and it did open my eyes about how much time I waste on a daily basis. We do it, but sometimes we just don’t think about it.

0 Votes  - +
Sometimes I Miss Iraq by VnutZ

Traffic is an amusing creature. This article seems coincidentally synchronized with my recent drive back to Augusta from Charleston. My wife and I were enclosed by moderate traffic on I-26 which included a plethora of very bad college drivers. At that moment, I was fondly reminiscing about having a .50cal turret gunner and signs warning other drivers to stay away lest they be fired upon lethally. There’s nothing like high velocity slugs bigger than my fingers to make this whole ‘traffic’ issue trivial.

When I lived in Atlanta, I noticed that the biggest influence on my commute time was whether or not the schools were in session. I looked up the statistics with the Georgia Department of Transportation, and they reported that school breaks resulted in a 5 percent decrease in traffic — such a small percentage made such a huge difference!

That’s when I realized that only small increases in ridership on public transit can make meaningful differences in traffic situations. Prior to that, while an avid public transit rider and proponent, I secretly believed that, in Atlanta’s case, it was too little and too late. I still believe it’s too little, but I think it’s not too late to implement more and create an impact.

I now live in Vienna, Austria. This is mass transit heaven. I’m sure the traffic still sucks, but I wouldn’t know because I never need a car!

Interesting experiment, but there are a quite few questions left.

You mentioned outliers and residuals. One of the first things to do would be to look at the travel time distribution to find the outliers, and then to repeat your ANOVA with and without outlier removal. The distribution of your data may or may not suggest a variance-stabilizing transformation. Another thing one could do is study the variance of the residuals. Yet another would be to fit smooth curves for travel-time versus departure time.

On basis of your data, several of us might be able demonstrate interesting approaches for analysis. In fact this is what often happens in science. Could you therefore please make the dataset available for download?

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statistics by Anonymous

OK, here’s the deal. i am a clincial psych graduate student who has taken numerous research methodology and stats classes so i may have a bit of a chip on my shoulder. i’m wondering about your choice of so many one-way ANOVA’s for the statistical analysis. you did 12, is that right? if you’re going to do more than 1 or 2, you need to do what’s called a Bonferroni correction in order to keep the type I error rate (the probability of making a conclusion that isn’t actually correct) in check. it’s quite simple really, if you want a .05 error rate for your experiment overall, you need to divide .05 by the number of statistical procedures you’re going to do. so, .05/12 = .00417 (approximately). you would use that as your new significance level for each ANOVA. i think some of your results turn out not to be significant when you use this cutoff level. check it out. also, there may be more powerful procedures you could use, such as MANOVA, etc. email me if you’d like to talk about it: k.e.graves@iup.edu

0 Votes  - +
Speed? by Anonymous

Very interesting article. One question, though – did you attempt to control your vehicle speed during your commute? I know for me personally, the later in the day I leave for work, the more likely I am to speed and/or drive recklessly in some other fashion (driving in Los Angeles from West L.A. to Downtown, I managed to make what would normally be 30-40 minute commutes in closer to 20 some times, depending on just how late I happened to be).

One of the reasons I mention this, is I feel that additionally drivers who are more likely to drive either fairly significantly above or below normal traffic pattern speeds would leave the most early. The fast drivers want to avoid traffic, while the slow drivers know they take more time to get to their destination. Then most of the drivers who stay closer to average speed would leave during ‘normal’ commute times… And then as you get closer to ‘late’ commute times (edging on leaving the house too close to 9 AM, I’d say) the average speed probably goes up again, with all of the commuters who need to try and avoid getting "the talk" from the boss ("Jim, we’ve noticed a disturbing trend in your willingness to arrive to work on time…").

Anyway, good stuff. Keep up the ‘borderline OCD’. :)

I’m from New Orleans, and I still live there. However, I have heard from many people that the traffic in Baton Rouge is much worse than it was before the storms of last year. I would imagine the same is probably true of Houston, too.

Have you considered taking another set post-Katrina? You’d just need to take a month, since the month-to-month variation was minimal, right?

0 Votes  - +
cell phones by shh...

Makes you wonder, how much would it improve if talking over the phone was forbidden in the US…

METRO is building a park and ride near the intersection of US 290 and Barker-Cypress Road. It should open sometime in 2007. But will it help you in your commute? Only if the buses that serve it also stop at the West Little York Park and Ride, allowing you an easy transfer to the 36 Kempwood, and even then the 36 Kempwood travels down Gessner which means you’d still have a hike along Clay Road to get to work.

Not everybody who lives in the Houston suburbs commutes downtown or to the Texas Medical Center, but that’s where all of METRO’s suburban park and ride routes go. METRO needs to hear from more suburb-to-suburb commuters such as yourself. Why are there no buses running up and down the Sam Houston Parkway? Where are the buses along Clay Road?

You pay your 1% sales tax to METRO. Commuters like you need to remind them that, while they’re busy trying to figure out how to build light rail down Richmond, suburban commuters such as yourself could use their help too!

Good article on Traffic, but I have to ponder for a moment, how much time did Brandon spend on this project for the results obtained? Another article to come I am sure. Fascinating read!

As for the conclusion, I am a little skeptical on Brandon’s attitude towards “mass transportation”. For one, how much of a delay in waiting for and making transfers would one have when riding mass transportation in comparison to driving and waiting in traffic as is?

I just ran across a crazy video made by some Georgia State students that decided they were going to obey the speed limit on Atlanta’s highways. It turns into a potentially dangerous experiment as drivers try to pass the slow moving bunch.

If everyone obeyed the speed limit it would kill our commute time!

Is Congestion Truly a Problem?
~~~~~~~~~~~~~~~~~~~~~~~
[ I am a long time critic of transportation policies in Texas, dating back to the early 1980s and the political fights over Metro’s rail plans and Harris County’s toll road schemes. I am sending this essay that I have been circulating far and wide. In it I try to explain my view that traffic "gridlock" is an exaggerated threat because traffic is demonstrably a self-limiting problem. The discussions about traffic have for too long been dominated by civil engineers who have a vested interest in the policies adopted by the public sector. The good news is that another point of view is getting an audience. If it succeeds in displacing the current view on traffic it means fewer design and construction contracts will be let and the political power of the engineers, architects, developers, etc. will be greatly weakened. By de-funding the established order in this way the path for political reform will be smoothed.

In the essay I argue that we do not need to plan for the future as much as road planners would have us believe. This was first emailed last September to transportation activists in the Houston region and then elsewhere because the analysis can be applied to every urbanized area. One fact I should have included is that the majority of new roads in the Houston region are built by the private sector. This includes new subdivision streets and sections of extended thoroughfares. Transportation agencies in the Houston area fail to point that out, probably because it is a fact that lowers the necessity of their function in the economy. I suspect this is probably just as true elsewhere. I think Texans can afford to let all or most road building devolve to the private sector. Here is an essay that shows that policy makers can rely on private interests to expand the road system more than they do:
http://www.independent.org/publications/tir/article.asp?issueID=43&articleID=544. It also explains how road building agencies can reduce their use of eminent domain. This approach obviously has implications for the TxDOT plan to crisscross Texas with the Trans-Texas Corridor and covert many existing roads to toll roads— Barry Klein]

—————————————————————————————————————
This is a message to my fellow citizens who are pursuing reform of Houston area transportation policies.
—————————————————————————————————————————
Several members of the Citizens Transportation Coalition and people interested in transportation issues have heard me talk about the work of transportation scholar Yacov Zahavi.

Mr. Zahavi died several years ago. He was from Israel, worked for USDOT, and for a time in the early 1970s worked in Houston with the Houston Galveston Area Council. Alan Clark, the head of HGAC’s transportation section today, knew him in those days.

Zahavi later worked for the World Bank. I am in touch with one of his former colleagues from that time who used to edit his papers.

Yacov Zahavi did some fundamental research that is ignored by US transportation planners but has the intellectual power to change the policy debates in Houston and elsewhere where "congestion" is treated as a community problem.

In fact, congestion is a subjective experience and people have different levels of tolerance for it. The Federal Highway Administration bluntly admits to this on its website. When individuals perceive themselves to have an intolerable congestion problem they usually find a way to resolve their problem. This phenomenon is unacknowledged by transportation planners.

There are 15,000 miles of road in Harris County. This network is a huge resource that allows for 100 million miles of travel during workdays. Much of the travel occurs at speeds above the legal limits, which is a sign the network is underutilized. Very importantly, this transportation resource allows individuals to adapt their travel activity based
on their personal goals and needs, and levels of patience with traffic.

Here are three examples of how individuals in different social roles adapt their use of the road network and allow the commute time to stay under half an hour. Workers often-times relocate (not hard for renters), adjust their work hours and even change employers when traffic becomes irritating. Employers will relocate to parts of the region that are less congested or that put them close to the workforce that they desire. Retailers play a role, because of their habit of looking for under-served pockets of consumers and then set up stores in their proximity, which incidentally reduces congestion by giving consumers shorter shopping trips.

All these factors combine to disperse traffic over the road network. They each play a role in the on-going, unplanned but never ceasing trends that mitigate congestion.

By these spontaneous adjustments the average home-to-work commute time in Harris County sits at 27 minutes, roughly where it’s been for the last several decades.
This information comes in a 2003 Census Bureau survey released this year
(www.census.gov/acs).

Back to Zahavi… Zahavi’s research defined the idea of the "Travel-Time Budget."
The research showed that…
a) most of the world spends about an hour a day in travel
b) most commutes are under half an hour, and
c) Families spend about 12-15% of their disposable income for mobility.

Here are two papers by contemporary scholars that draw on Zahavi’s analyses.

The Evolution of Transport
Jesse H. Ausubel and Cesare Marchetti
The Industrial Physicist 7(2):20-24, April/May 2001.
http://phe.rockefeller.edu/TIP_transport/

Toward Green Mobility: The Evolution of Transport
Jesse H. Ausubel, Cesare Marchetti, and Perrin S. Meyer
European Review 6(2):143-162, 1998.
http://phe.rockefeller.edu/green_mobility/

This is a quote from the first paper:
"for humans, a large accessible territory means greater liberty
in choosing the three points of gravity in of our lives: the home, the workplace, and the school.
Four-fifths of all travel ends in this ambit."

Mr. Ausubel, in an email to me several months ago, made this statement…

"We envision a transport system producing zero emissions and sparing the
surface landscape, while people on average range hundreds of kilometers
daily. We believe this prospect of "green mobility" is consistent in general
principles with historical evolution. We lay out these general principles,
extracted from widespread observations of human behavior over long periods,
and use them to explain past transport and to project the next 50 to 100
years. Our picture emphasizes the slow penetration of new technologies of
transport adding speed in the course of substituting for the old ones in
terms of time allocation. We discuss serially and in increasing detail
railroads, cars, airplanes, and magnetically levitated trains (maglevs)."

This is a link to a website featuring Zahavi’s papers:
http://www.surveyarchive.org/zahavi.html

A google search on Yacov Zahavi name will result in over 200 hits.

I think that the more people become aware of Zahavi’s work and its impact on the thinking of a number of urban scholars the more quickly will spread the understanding that congestion can actually be thought of as "self-limiting."

Here are three links to pages on the Federal Highway Administration website to see more about how the FHWA views congestion: http://www.fhwa.dot.gov/congestion/, http://www.fhwa.dot.gov/congestion/congsame.htm, and http://www.fhwa.dot.gov/congestion/congwhat.htm. On the last look for the link to Rethinking Traffic Congestion by Brian Taylor, the Director of the Institute of Transportation Studies at UCLA.

Several hundred Houston area residents will soon be engaged in an extensive regional planning process being conducted by HGAC with help from several local sponsors, including Blueprint Houston. By bearing in mind the work of Zahavi and his disciples, participants can treat with skepticism the description of future traffic conditions as projected by the men and women whose careers and incomes are tied to the idea that gridlock is Houston’s destiny unless billions of dollars in new road and transit facilities are constructed.

Henceforth, Houston area residents can do their infrastructure planning freed of the grip of "traffic panic." New planning options are thereby opened up for consideration.

Yacov Zahavi’s research allows Houstonians to approach transportation questions much more calmly than we have in the past. Being aware of its existence means that, as we reach our private and collective conclusions on proposed infrastructure projects, our thinking can be based on a wider range of growth scenarios for the Houston area.

— Barry Klein
Public Policy Consultant
713-224-4144

Predictability
There has been and most likely will continue to be criticisms of this article in the area of "predictability." However, those critics are missing something important. Everyone and their dog seems to have a theory about traffic, and all of those are unsupported. The main point of my article was to analyze data to come up with my conclusions, not just throw out the usual conjecture. Data provides means for change that were previously lacking.

General Traffic Tips
With that being said, I’d like to take this opportunity to throw out a couple of things that I’ve noticed that result from my same obsession with efficiency, but that are not from the concrete data in the article.

I’ve come to think that traffic is at the mercy of merging; it slows down when cars merge in or when traffic merging off is backed up. If the effects of merging could be harnessed, I think traffic flow would be smooth – or at least much smoother. It wouldn’t always be fast, of course, but it would still work. (Reference the Discovery Channel podcast cited in the article that discusses the fact that highway traffic still "works" at low speeds – all the way down to around 15-20mph. Although things are slow, the cars are close together and a high volume of cars is still moved through. Below 15 mph, however, things jam up and the system fails.)

Some cities have attempted to regulate this by using "metered" highway ramps. They have this in Houston along my commute route, and I’ve witnessed just how much it can help keep things smooth. On more than one occasion, I’ve seen a smooth, metered merging interface turn into a mangled mess about 15 seconds after the metering traffic lights on the onramps turned off. With one car entering about every 5-10 seconds, the flow could easily adapt. But, when they were turned loose and all bombarded the first lane, the traffic flow was broken and a backwards "wave" of brakelights began to propogate on the freeway. With this happening at every onramp, it’s easy to see how it would translate into widespread gridlock – at least until the traffic volume was low enough for it not to matter.

I think that time can be saved on the commute by working these merging interfaces correctly. Immediately before an onramp, traffic is usually slow in the first lane. A little further before it, however, traffic is usually lighter, as people don’t want to deal with merging vehicles. Immediately after an onramp, traffic is also lighter, as those getting on want to quickly move into the "fast" lanes. So, my strategy is to stay in the "slow" lane all the way until the threshold where it starts to be affected by the incoming vehicles. I then switch into the adjacent lane until just past the onramp and then switch back. I’ve had reasonable success in this, but sometimes, of course, I get stuck in the first lane and have to wade through the merging traffic.

Another thing I do to cut time off of my commute is to go around particularly bad intersections, if possible. This is laid out in note 12 of my article, where I describe two "longcuts" where I drive around intersection backups.

More Data Interpretation and Use
I wanted to comment a little more on using the data presented here.

  • School schedules – Although knowing your school schedule will allow you to predict when traffic will be worse, it is difficult to use that information the morning of to save commute time. Instead, this information is best used to get involved on the school board or with the city council, as well as your employer. School and work start times are somewhat flexible, and my data shows that moving them around could have a significant and immediate impact on commute times. Not only that, but studies have shown that later start times for high schoolers result in improved scholastic performance.
  • Fridays – It is interesting to note that the traffic-easy Friday mornings don’t carry over to the afternoon. The only explanation that I could think of is that there are fewer people at work, but that the number of people going home within rush hour actually ends up the same, as many of those who might usually leave late leave on-time for the weekend. This information could actually be useful to the commuter; if he/she wanted to have a relaxing Friday night, maybe it would be better to work a little longer and have a relaxing drive home.
  • Flexibility – I didn’t stress how the morning and evening commute times aren’t completely independent (although they could be if you have no working hours constraint). My data shows that moving the morning departure time out has a greater effect than moving the evening time out, so the morning adjustment took precedence. The morning departure time is also more directly in my control, as things can come up at work that could mess up plans. I could have changed my commute to be leaving really early, but I think that plan is wrought with problems. Any issues at home or work causing you to leave later than anticipated would result in an increase in your commute time – actually compounding the problem. Because I leave after the traffic peak, any delays – both in the morning or the evening – result in a decreased commute time. This means that not only do you end up being not quite as late as you thought, you also avoid multiplying the "I’m late" stress with the "traffic sucks" stress.
  • Employer Incentive – Speaking of stress, another important point in all of this is to realize that employers have the potential to increase worker performance by allowing them flexibility in their arrival/departure times. Just as high school students can perform better when school times are adjusted, employees will most likely work better when they don’t have to deal with the stress of a harsh commute. The data I provided would allow an employee to go to his employer and push for change. In my case, my first attack front is going to be the public transportation system. If that falls through, plan B is to try and effect change with my employer. I haven’t secured a concrete proposal, but one idea I’ve had is for them to work together with other area businesses to provide private bussing from major subdivisions or the existing park-n-rides. The money spent there could easily make up for itself in reduced employee stress, attracting future employees, and a reduced parking need on-location.
1 Vote  - +
Going green by Anonymous

I’ve been fascinated by this subject since living in the Washington, DC area. Averages for commute times are misleading because they include “outside the heavy traffic corridor” drivers in the metro area. We need to understand how much it is costing us – in time, emissions, and fuel – for the millions who may spend an additional 30 minutes EACH way to get to work every work day. That’s double the time it would take with little traffic. In this analysis, we’d need to drive from home to work during a 0 or little traffic time and compare that against the average for normal work hours. I suspect the difference is in the 30-60 minute range for a large group of drivers. (again, back to not using the metro area average) What’s important is that we understand this contribution to time away from our families, green house gas emissions, fuel costs, and health – DIRECTLY. We need to juxtapose this analysis – which applies to millions of Americans – against the money we provide for roads through gas taxes and other types of taxes. Are we getting our money’s worth? Is this a good place (reducing travel time in the “rush hour” time frame) to invest our green dollars?

Great work, nice idea for an app, with gps and acurate clock

http://tinymailto.com/oliversl

I live in Houston also, so I feel your pain on the traffic. I turn into Archie Bunker when I get behind the wheel trying to drive through town (ha ha that would be a good Snickers© commercial). I think a lot of the problem with Houston roads is they are an engineering nightmare.

As an example I will use I-45 South and the 610 Loop. This intersection has been around for years but it is one of the highest congested areas in Houston ( there are several others). Where the intersection fails is the design. The problem is even with the new roads/tollways going up the people engineering the roads are making the same mistakes.

If you’re heading East on 610 South and want to exit I-45 South starting around 2:30ish PM to almost 8PM you would be stuck in traffic for quite a bit of time. The reason, they only have 1 (one) lane for traffic, then you have to merge into traffic heading South on 45. The explosion of population increased the traffic (this should be common sense) but the roads have remained the same. And they will continue to happen because they (road construction) continue to build the new roads the same way.

I-10 East to Beltway-8 North(Sam Houston Toll-road) Fairly new, I remember it being built and not sure of the age within the last 15 years, the exit is 1 (one) lane and it’s already starting to see the same congestion as I-45 and 610 Loop.

Then compile truckers/slow drivers(the people who drive 20-30 miles under the speed limit) weather, inconsiderate drivers (the ones who don’t get into the turn lane and cut in right before the barrier so that everyone slams on their brakes.)

There are several solutions for this but that’s another time.

Good read and good formulating. Look forward to frequenting this site daily.

0 Votes  - +
_ by Anonymous

Loved the article. And OCD inspired you to do things with interesting results.

Brandon, interesting analysis. I’ll leave my comments short, but it seems that you are very tied to a suburban way of life. Shortening your commute can be much easier – live closer to work (while close enough to food, entertainment…)- that is live in a dense neighborhood with a high walkability score.
Commute by bicycle.
Commute by foot. You’ll find your commute much more consistent with bicycle or foot commutes.

0 Votes  - +
merging traffic by Anonymous

the law should be in merging situations – each car lets one car merge before it. allowing orderly progress and everybody knows exactly what to do.

I received this comment via email.


A co-worker sent your article link to me – interestingly I’d been to the site for my first time on Sunday reading about 6-wheeled F1 racecars on Saturday evening.

It’s interesting that your article discusses transit issues in Houston (which I believe has no zoning laws).

My commute takes me from north central Columbus, OH to south central Columbus, OH. It takes me almost exactly 30 minutes each day – and roughly half (time-wise) of my commute each way is “reverse”.

As it turns out, I live at the north end of the COTA (Columbus’s only public transit option) #4 bus route and work at the south end of same.

Google Maps puts my non-rush hour transit time at 1 hour each way by bus (compared to 1 hour 19 minutes on a bike!).

I’ve worked as a Consultant in Columbus since 2007 and every time I was placed at a new assignment I re-assessed the bus/car option factoring in fuel/maintenance/parking/safety/time/fare/walk. The bus was not a viable option and in most cases faced a limited schedule with a one mile or more walk each way on the work end. When I have ridden the bus to work, our bus has been held hostage for minutes at a time while potential riders panhandled for fare.

When I attend community meetings on public transit – the desire for improvements seems to be unanimous. The trouble is that due to Columbus’s strict Zoning laws it is impossible to achieve population densities that would make a more pervasive transit system sustainable.

Meanwhile the big transit story here is a government effort to build a $400 million commuter rail line (think double-decker bus on rails) that takes 12 hours to make a trip from Cleveland to Cincinnati that takes 6 hours at most by car. The service would require a $14 million/yr. operational subsidy and employ less than 100 people statewide – most of them low-wage service staff.

The main transit problem here is the urban sprawl caused by our Zoning laws, and I don’t see an end to those anytime soon because everyone is terrified that their neighbor will build a steel foundry or garbage dump right next to their house in the suburbs. You have that problem a lot in Houston – right?

Regards,
Mark


Mark,

I haven’t seen many foundries or garbage dumps next to houses – but I live in the suburbs and only really “experience” a small portion of Houston on a regular basis. There are businesses adjacent to neighborhoods, but it seems this is done in an orderly fashion.

I live and travel this same area every day also, with one very important differance, I drive the other way, out of town in the morning, and in at night. Picked my house knowing this made a hugh differance. While I’m rolling out 290 at 65 mph in the morning, you guys are bumper to bumper.

Mass transit is a centrally planned idea that is only supported by studies funded by mass transit companies . The transit oligarchy, which is made of companies that build rails, sell trains, or property, contribute to the campaigns of politicians who then fund un reviewed studies which say “mass transit good.” Which is what would be expected. When those studies are reviewed by consumer groups, they are exposed as shams. The new budget is still approved anyway.
True improvement will occur when the german system of collision avoidance on cars becomes common. The system uses tiny radars and broadcasts info to other cars. This is a diversified system.
It is the difference between a huge central computer, which was planned in the 60s and 70s, and small separate computers which communicate with each other.
The dispersed system is almost always better than the centrally planned and controlled system. mass transit is not the answer

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