Potential Increase in Truck Traffic by Reducing Car ...

Effect of Removing Passenger Car Volume on Freight Movement: A Simulation Study Gregory A. Harris and Bernard J. Schroer University of Alabama in Huntsville Huntsville, AL USA

Dietmar P.F. Moeller University of Hamburg Hamburg, Germany

ABSTRACT The focus of this project was to determine the congestion point and the potential for increased truck traffic if there is a decrease in passenger traffic for a segment of interstate I-65 north from Montgomery to Birmingham, Alabama. In summary, current peak hourly traffic can increase 20% before congestion occurs at one of the road segments, Segment E181-186. This segment was the first road segment just after the interstate reduced from three to two lanes (congestion occurs when the volume to capacity ratio reaches 75%). A small percentage decrease in car traffic allows for a much greater percentage increase in truck traffic because of the traffic volumes. Based on the simulation results, if passenger car traffic is decreased 5% entering the first segment, the truck traffic can increase 30% before congestion occurs. If passenger car traffic is decreased 10% entering the first segment, the truck traffic can increase approximately 55% before congestion occurs. Included in this paper are a description of the simulation model, the experimental design and an analysis of the simulation results.

the bordering states have also added assembly plants. The Kia assembly plant in on the Georgia Alabama border near Auburn/Opelika. The VW plant is under construction in Chattanooga. The Toyota plant is under construction in Mississippi just west of Florence AL.

KEYWORDS Interstate, simulation, congestion, trucks, traffic, discrete event

3 PREVIOUS RESEARCH The most frequently used tool to model interstate traffic is Corridor Simulation, CORSIM (FHWA, 2009). CORSIM is a microscopic simulation program developed by the FHWA to assess both freeway and arterial traffic flow conditions. CORSIM applies time step simulation to model traffic flow and models individual movements based on complex car following, gap acceptance and lane-changing theories. CORSIM also incorporates randomness that can occur within a network by including different types of driver, vehicle and traffic system characteristics.

1 INTRODUCTION The automotive manufacturing industry in Alabama has grown rapidly since the mid 1990s. Alabama now has three vehicle assembly plants and 285 supplier plants with total employment of over 45,000 direct manufacturing jobs. Alabama also has four engine plants with two of the plants located at the assembly plants. Annual capacity is 760,000 vehicles and 1,000,000 engines. The three assembly plants are Mercedes-Benz in Tuscaloosa, Honda in Lincoln and Hyundai in Montgomery. The four engine plants are Honda, Hyundai, Toyota in Huntsville and Navistar International Diesel in Huntsville. In addition,

The automotive manufacturing industry has been locating near the interstates to assure just in time deliveries. As a result interstate I-65 has become a major truck route through Alabama as well as north through Tennessee, Kentucky and Indiana. 2 STUDY OBJECTIVE Traffic planners are concerned that interstate I65 will soon be reaching the point where the vehicle to capacity ratio exceeds 75%, the point that is considered congestion. The objective of this project was to determine the congestion point. A second objective of this project was to determine the increase in truck traffic if there is a decrease in passenger car traffic entering interstate I-65N from Montgomery, AL.

Schroer, et.al. (2009) have developed a conceptual framework for rapidly simulating interstate traffic using ProcessModel (1999) as

compared with CORSIM, which is more difficult to use, and more time consuming. Match Line Cars: 2134 Trucks: 377 Cars: 80 Trucks: 35

Cars: 329 Trucks: 15

Exit 200


Cars: 60 Trucks: 20

Cars: 2092 Trucks: 429 Cars: 72 Trucks: 15


Exit 186





Exit 181


Exit 219




Exit 179


Exit 212


Cars: 3,031 Trucks: 413


Exit 176



Exit 208




Exit 173

Exit 205



65

Exit 228

Cars: 50 Trucks: 15



Exit 231

Cars: 1,922 Trucks: 394 Match Line

Figure 1. Peak hourly traffic on Interstate 65N from Montgomery to Birmingham 4 PROCESSMODEL The simulation model was written in ProcessModel (1999) and is a modification to a previous model used to simulate the traffic on interstate I-65 north from Montgomery to Birmingham, AL (Anderson, et.al., 2010). Since this model is a modification to a previous model, no model verification and validation was necessary. 5 EXPERIMENTAL DESIGN Table 1 gives the experimental design. The Baseline Run1 simulates the current peak hourly

traffic going north from Montgomery. At peak traffic 3,770 cars and 419 trucks arrive every hour at the start of the ProcessModel (See Figure 1). Of these numbers 840 cars and 110 trucks use Exit 173. Therefore 2,930 cars and 309 trucks actually enter Segment E-173-E176. Runs2-3 increased the number of car and truck arrivals entering the model at the first segment by 10% and 20%, respectively. For Run2 3,223 cars and 340 trucks enter Segment 1. For Run3 3,516 cars and 371 trucks enter Segment 1. Runs4-10 are modifications to Run3. Runs4-6 represent a 5% decrease in cars entering at Segment 1 and a corresponding 20%, 30% and 40% increase in trucks entering at Segment 1. Runs7-10 represent a 10% decrease in cars entering at Segment1 and a corresponding 40%, 50% and 60% increase in trucks entering at Segment 1. Table 1. Experimental design Run Description Baseline Peak hourly traffic Run1 Run2 10% increase in vehicles entering at Segment E173-E176 Run3 20% increase in vehicles entering at Segment E173-E176 Run4 5% decrease in cars entering and 20% increase in trucks entering at Segment E173-E176 Run5 5% decrease in cars entering and 30% increase in trucks entering at Segment E173-E176 Run6 5% decrease in cars entering and 40% increase in trucks entering at Segment E173-E176 Run7 10% decrease in cars entering and 30% increase in trucks entering at Segment E173-E176 Run8 10% decrease in cars entering and 40% increase in trucks entering at Segment E173-E176 Run9 10% decrease in cars entering and 50% increase in trucks entering at Segment E173-E176 Run10 10% decrease in cars entering and 60% increase in trucks entering at Segment E173-E176

6 BASELINE RUN1 RESULTS Table 2 gives the Baseline Run1 results. No congestion occurred at any of the road segments. All volume/capacity ratios were less than 65% with many of the rural segments below 50%. Congestion is defined when the volume/capacity ratio equals or exceeds 75%. Table 2. Baseline Run1 results Road Length Average Segment (miles) Speed (mph) E1733 60 E176 E1763 62 E179 E1792 63 E181 E1815 58 E186 E18614 62 E200 E2005 61 E205 E2053 62 E208 E2084 63 E212 E2127 65 E219 E2199 64 E228 E2283 53 E231 E2317 61 E238

Volume / Capacity Ratio (%) 52

7 RUNS2-3 RESULTS Table 3 gives the results for Runs2-3. Congestion occurred reaching 75% at Segment E181-186 for Run3. This is the first segment just after the interstate is reduced from three to two lanes. All other road segments are well below the congestion levels.

46 41 64 54 48 44

Table 3. Run2-3 results Road Average Segment Speed (mph) Run2 Run3 Increase +10% +20% in traffic E17360 58 E176 E17661 60 E179 E17962 61 E181 E18157 55 E186 E18661 62 E200 E20062 60 E205 E20563 61 E208 E20861 62 E212 E21264 64 E219 E21964 63 E228

Volume/Capacity Ratio (%) Run2 Run3 +10% +20% 55

63

49

54

43

48

68

75

56

61

50

54

45

49

40

44

37

40

42

45

39 36 41 54 53

8 RUNS4-10 RESULTS Tables 4 and 5 give the results for Runs4-10. Figure 2 gives the volume to capacity ratios for road segment E181-186 where congestion occurs. Congestion did not occur in Run4 with a 5% decrease in car traffic and a 20% increase in truck traffic. However, congestion occurred in Run5 with a 5% decrease in car traffic and a 30% increase in truck traffic, reaching 75% congestion. Congestion did not occur for Runs7-9 with a 10% decrease in car traffic and a 30%, 40% and 50% increase respectively in truck traffic. However, congestion occurred at Run10 with a 10% decrease in car traffic and a 60% increase in truck traffic, reaching 76% congestion. Congestion probably occurred around the 55% increase in truck traffic.

Table 4. Runs4-10 results (average speed)

Run4 Decrease in car traffic Increase in truck traffic E173-E176 E176-E179 E179-E181 E181-E186 E186-E200 E200-E205 E205-E208 E208-E212 E212-E219 E219-E228 E228-E231 E231-E238

Run6

Average Speed (mph) Run7 Run8 -5% -10% -10%

-5%

Run5 -5%

+20%

+30%

+40%

+30%

58 60 61 55 60 59 60 62 64 63 53 60

58 60 61 55 60 60 60 62 64 63 53 60

58 59 60 54 60 59 60 62 64 63 54 60

58 60 61 55 61 60 61 62 64 63 53 60

Run9 -10%

Run10 -10%

+40%

+50+

+60%

58 60 60 55 60 60 60 62 64 63 53 60

58 60 60 55 60 60 60 62 63 63 53 60

58 59 60 54 60 59 60 62 64 63 54 60

Table 5. Runs4-10 results (volume/capacity ratios)

Decrease in car traffic Increase in truck traffic E173-E176 E176-E179 E179-E181 E181-E186 E186-E200 E200-E205 E205-E208 E208-E212 E212-E219 E219-E228 E228-E231 E231-E238

Run4 -5% +20% 61 54 47 73 62 55 49 44 40 45 57 57

Run5 -5% +30% 61 54 47 75 62 54 49 43 39 45 57 56

Volume/Capacity Ratio (%) Run6 Run7 Run8 -5% -10% -10% +40% +30% +40% 62 58 59 55 51 52 49 46 47 72 74 78 64 59 61 55 52 53 49 47 48 44 42 43 40 39 39 45 44 44 57 56 56 57 56 56

Run9 -10% +50% 60 52 46 74 61 54 48 43 39 45 56 56

Run10 -10% +60% 61 54 47 76 62 55 49 44 40 45 57 57

Volume/Capacity Ratio (%)

78 76

Congestion at 75%

74 72 70 68 Run

1

4

5

6

7

8

9

10

- cars 0% -5% -5% -5% -10% -10% -10% -10% + trucks 0% +20% +30% +40% +30% +40% +50% +60%

Figure 2. Volume to capacity ratios for segment E181-E186 9 CONCLUSIONS In summary the following conclusions are made: •

•

•

•

Current peak hourly traffic can increase 20% before congestions occurs at Segment E181-186. This segment was the first road segment just after the interstate reduced from three to two lanes (congestion occurs when the volume to capacity ratio reaches 75%). If passenger car peak hourly traffic is decreased 5% entering the first segment, the truck traffic can increase 30% before congestion occurs. If passenger car peak hourly traffic is decreased 10% entering the first segment, the truck traffic can increase approximately 55% before congestion occurs. Many of the rural segments had low congestions below 50%. The only bottleneck is the E181-E186 road segment.

10 ACKNOWLEDGEMENTS This research was sponsored by the U.S. Department of Transportation, Federal Transit Administration, Project No. AL-26-7262-00. 11 REFERENCES Anderson, M., G.Harris, B. Schroer, M. Spayd and D. Moeller, 2010: Discrete Event Simulation of Interstate Traffic: Comparison with CORSIM, UAH Research Report, 2010. Huntsville, AL. Federal Highway Administration, 2009: Federal Highway Administration Traffic Analysis Tools Program, http://www.ops.fhwa.dot.gov/trafficanalysistools /ngsim.htm ProcessModel, 1999: Users Manual, ProcessModel Corp., Provo, UT. Schroer, B., G. Harris and D. Moeller, 2009: “Conceptual Framework for Discrete Event Simulation of Interstate Traffic,” Proceedings 2009 Huntsville Simulation Conference, Huntsville, AL, October.

12 BIOS Gregory Harris is Director of the Center for Management and Economic Research at the University of Alabama in Huntsville (UAH). He is a certified NIST lean manufacturing trainer. He holds faculty positions in the College of Engineering and the College of Business at UAH. Harris has a Ph.D. in Industrial and Systems Engineering from UAH and is a registered Professional Engineer. Bernard Schroer is Principal Research Engineer at UAH. He is a Fellow of IIE, a Fellow of the

SME and a member of SCS. He has a Ph.D. in Industrial Engineering from Oklahoma State University and is a registered Professional Engineer. Dietmar Moeller is Professor of Computer Science and Computer Engineering at the Mathematics, Computer Science and Science Faculty of the University of Hamburg, Germany. He also serves as Chair of Computer Engineering. Moeller has a Dr.-Ing. (Ph.D.) in electrical engineering and control theory from the University of Bremen.