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«BASIC HUMAN DECISION MAKING: An Analysis of Route Choice Decisions by Long-Haul Truckers John Holland Knorring Advisor: Professor Alain L. Kornhauser ...»

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7 Conclusion The goal of this study was to do an empirical analysis of long-haul trucker route choice decision-making as they navigate the U.S. highway network. This study looked at the basic decision making process that the drivers demonstrated. This study examined areas in the U.S. highway network where truckers were faced with a decision between two different routes: a downtown route that was passed through, or very close to, the center of the city, and a bypass route the skirted the edge of the city. By examining the decisions shown by the truck drivers it is possible to determine the manner at which drivers trade off distance and time. This was accomplished by building a Logit model that was calibrated with data from almost 250,000 trucks over a 13-day period. The logit model predicted the percentage of trucks that used the bypass route as a function of the perceived speed on the downtown route.

Using the perceived speed curves, this thesis attempted to explain the decision making process in terms of rationality of decision-making. This thesis showed in the previous chapter that truck drivers are primarily time minimizers. (If the drivers were distance minimizers, then the route choice decisions would be made for them and there would be no need for further study.) Supposing that drivers are time minimizers, it is now interesting to examine the decision-making process. Kahneman would say that the drivers are irrational because they pick routes at random. They should know what they speed is on each route before making a decision if they are rational drivers. Since they have no basis for the actual conditions on either route, any decision that they make is made irrationally. However, this thesis proposes that the decision making process is more scientific than a simple 50/50 coin flip when drivers are faced with uncertainty.

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This thesis proposes that truck drivers usually do not just make a guess about which route to take when faced with similar routes. Instead, the drivers make route choice decisions based on the their respective perception of each route. Their perceptions are generated from a number of factors including past experience on the route, time of day, current traffic conditions, and knowledge of the route to name a few. The driver puts all of these factors together, along with other non-quantifiable factors to generate an idea for the expected time of the trip. With this information, the driver then derives the perceived speed on the downtown route. With this perceived speed, the driver then subconsciously compares the result with the perceived speed on the alternate route. With these speeds, the driver then computes the time spent on each route. The minimum of these two quantities results in the time minimized, utility maximized, optimal solution. The explanation for the need for a probability of usage on the bypass route results in the fact that different people have different perceptions of the route in front of them. One driver may have taken both routes previously with one route being significantly better than the other route. A different truck driver may have had an entirely different experience, or no experience previously. As a result, the second driver is going to most likely have different perceptions of the route in front of his truck.

7.1 Interpretation of the Results The results of this study do not conclusively show that truck drivers make rational decisions with respect to their perceptions. The data does however show that time is a significant factor in the decision making process. It is impossible to tell exactly how much more significant time is in the decision making process then distance is due to the

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dimensionless nature of utility, however, it is likely that time plays a more important role than distance.

7.2 Areas for Further Study While this thesis did not provide a strong proof to explain the decision making process of long-haul truckers, it did shed some more light on the more important factors in the decision making process. It is also safe to say that there is more to be done with respect to this study to come to more meaningful conclusions. The three most obvious areas that would benefit from further study are the determination of stops, the frequency of data collection, and improving on the Box Algorithm.

Todd Burner faced many of the same problems associated with stop determination.

It is really difficult to determine, from this data set, if a truck is stopped. Stop determination is very important because it is likely that there are trucks in the study that should not be analyzed. The goal of this study was to examine route choice decision making of trucks that were passing through a city without stopping. Having these extraneous trucks in the sample could potentially alter the results of the study.

Along the same lines of stop determination, if the frequency of data collection were increased, there would be fewer problems with “bad data” making it into the group to be analyzed. Having more frequent location observations would allow for easier stop determination in addition to simplifying the counting of the trucks on each route. The results would be more precision in model calibration stages and a more accurate picture in general.

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Lastly, the Box Algorithm must be updated. Currently, the Box Algorithm misses far too much data. Additionally, the Box Algorithm returns slightly biased data. Because of these facts, it is vital that for further analysis the next analyst should improve on the technique of selecting the subset of trucks used in the study.





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Bibliography

1. Abu-Eisheh and Fred Mannering. Discrete/Continuous Analysis of Commuters’ Route and Departure Time Choices. In Transportation Research Record 1357, TRB, National Research Council, Washington, D.C., 1992

2. Ashok, K. and M. Ben-Akiva, "Alternative Approaches for Real-Time Estimation and Prediction of Time-Dependent Origin-Destination Flows," Transportation Science, Vol. 34, No. 1, February 2000.

3. Beckman, Martin, McGuire, C.B., Winsten, Christopher B. Studies in the Economics of Transportation. New Haven: Yale University Press, 1956.

4. Ben-Akiva, M. and M. Bierlaire, "Discrete Choice Methods and Their Applications to Short Term Travel Decisions," The Handbook of Transportation Science, R.W.Hall ed., pp. 5 - 33, 1999.

5. Ben-Akiva, M., D. McFadden, T. Garling, D. Gopinath, J. Walker, D. Bolduc, A.

Borsch-Supan, P. Delquie, O. Larichev, T. Morikawa, A. Polydoropoulou, and V.

Rao, "Extended Framework for Modeling Choice Behavior," Marketing Letters, Vol. 10, No. 3, 1999.

6. Ben-Akiva, M., Richards, Martin G., A Disaggregate Travel Demand Model, DC Heath Company, Toronto, 1975.

7. Ben-Akiva, M., Structure of Passenger Travel Demand Models, Massachusetts Institute of Technology, 1973.

8. Brog, Werner and Arnim Meyburg. Nonresponse Problem in Travel Surveys: An Empirical Investigation. In Transportation Research Record 775, TRB, National Research Council, Washington, D.C., 1980.

9. Bruzelius, Nils. The Value of Travel Time: Theory and Measurement. Great Britain: Croom Helm Ltd., 1979.

10. Burner, Todd. Exploring the Effects of Anticipated Congestion on Truck Driver Route Choice Behavior. Princeton University department of Civil Engineering and Operations Research, 1999. pp.1-90.

11. Domencichi, Thomas and Daniel McFadden. Urban Travel Demand: A Behavioral Analysis. Netherlands: North-Holland Publishing Company, 1975.

12. Farrell, Jay A. and Barth, Matthew. The Global Positioning System & Inertial Navigation. McGraw Hill, New York, New York, 1999.

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13. Kaplan, Elliot D. Understanding GPS: Principles and Applications. United States of America: Artech House, Inc., 1996

14. Meyburg, Arnim H., Stopher, Peter R., Behavioral Travel Demand Models, DC Heath Company, Toronto, 1976

15. Nicholson, Walter. “Microeconomic Theory: Basic Principals and Extensions” Harcourt College Publishers. Fort Worth, TX. 1998.

16. Oppenheim, Norbert, Urban Travel Demand Modeling: From Individual Choices to General Equilibrium, John Wiley & Sons, New York, New York, 1995

17. Papacostas, C.S. and P.D. Prevedouros, Transportation Engineering and Planning, 2nd edition, United States: Prentice-Hall Inc., 1987, pg. 258.

18. Poorman, John and Deborah Stacey. Self-Administered Mailback Household Travel Survey. In Transportation Research Record 955, TRB, National Research Council, Washington, D.C., 1984

19. Sebraneck, Kemper, Meyer, Write for College, Houghton Mifflin Company, Wilmington, Massachusetts, 1997.

20. Sheffi, Yosef, Urban Transportation Networks: Equlibrium Analysis with Mathematical Programming Methods, Prentice Hall, Englewood Cliffs, New Jersey, 1985.

21. Stopher, Peter R. Meyburg, Arnim H., Behavioral Travel-Demand Models, DC Heath and Company, Washington, D.C., 1976.

22. Ross, Sheldon. Introduction to Probability Models: Seventh Edition, Harcourt Press. San Diego, CA, 2000.

23. Transportation Research Board, Behavioral Demand Modeling and Valuation of Travel Time, National Research Council, Washington D.C., 1973.

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Appendix 3: C Code for Data Analysis The following C code was written by Christopher Chou ’03 to be used by John Knorring ’03.

// knorring_travel.cpp : Defines the entry point for the console application.

// by ctchou 3-28-03 // Program parses a list and selects certain combinations using an FSM // Make sure that the end of file is at the last line!!!

Imports System.Math #include "stdafx.h" #include string #include fstream #include iostream #include vector #include windows.h #include mmsystem.h #include sys/types.h #include sys/stat.h #include io.h #include stdio.h #include stdlib.h #include cmath using namespace std;

FILE *stream3;

FILE *stream4;

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int main(int argc, char* argv[]) { ifstream infile;

int line_count=0;

int length;

int state = 0;

int i,j;

int total_travel_time = 0;

int flag_3 = 0; // went through 3 int flag_4 = 0; // went through 4 char truck[6] = {0}; // old truck id char new_truck[6]; // current truck id char first_timer[13]; // starting time char end_timer[13]; // ending time char first_lat[25] = {0};

char first_long[25] = {0};

char last_lat[25] = {0};

char last_long[25] = {0};

char route;

string input;

basic_string char::size_type truck_index;

basic_string char::size_type truck_index_2;

int FD3; // file descriptor for file containing route 3 int FD4; // file descriptor for file containing route 4 int test_count = 0;

double distance_diff;

double RAD =.01745566;

// change data destination... 4 spots FD3 = _creat( "Cincidata3.txt", _S_IREAD | _S_IWRITE );

if(FD3 == -1 ) perror( "Couldn't create data file" );

else { printf( "Created data file.\n" );

_close(FD3);

}

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////////////Change Distance Diff////////////// distance_diff = 16.4; //// CHANGE FOR EACH FILE //////////////////////// INPUT FILE /////////////////////////// infile.open("CincinattiTruckIDs.txt"); // make sure it's txt //////////////////////////////////////////////////////////////////

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route=input[length-1];

// find the truck number truck_index = input.find_first_of('-',0);

input.copy(new_truck, 6, truck_index+1); // truck is the truck number (in chars)

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// distance_c = 60* (acos((sin(first_lat_d)*sin(last_lat_d)) + (cos(first_lat_d)*cos(last_lat_d)*cos(abs_long))));

p1 = sin(first_lat_d) * sin(last_lat_d);

p2 = cos(first_lat_d) * cos(last_lat_d);

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fprintf(stream4," "); // formatting // print out start time fprintf(stream4,"%c", first_timer[0]);

fprintf(stream4,"%c", first_timer[1]);

fprintf(stream4,":");

fprintf(stream4,"%c", first_timer[2]);

fprintf(stream4,"%c", first_timer[3]);

fprintf(stream4,":");

fprintf(stream4,"%c", first_timer[4]);

fprintf(stream4,"%c", first_timer[5]);

fprintf(stream4,"%c", first_timer[6]); // space

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} line_count++;

//cout line_count; // if you want, output total, may be problems, change to double }while(infile.eof()==0);

infile.close();

fclose(stream3);

fclose(stream4);

//End Code for reading files

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7.2.12 Wilmington, DE

143 Knorring

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