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Traffic FlowExploring dynamic vs. static toll pricing ina traffic network simulation modelNora ShoraLaura TatschSpring 2006EMIS 4395Senior DesignDr. BarrShora - TatschTable of ContentsManagement Summary 3Background & Description of the Problem 3Analysis of the Situation 4Technical Description of the Model 5Analysis & Managerial Interpretation 6Conclusions & Critique 15Appendix A 17Appendix B 18Appendix C 19Appendix D 20Appendix E 23-2-Shora - TatschManagement SummaryMany cities across the world have experienced, and are currently experiencing, increased traffic on highways and urban networks. At the same time, roads and highways have a limited capacity and are only capable of transporting a limited number of travelers. An increase in the number of travelers has increased all of the following factors associated with travel: Travel time Number of stops Travel costs Delays Air pollution Accidents Noise levelThe first four on this list are factors that we will use in our investigation of the traffic flow problem. Road pricing is one tactic used as an effective demand management strategy to reduce traffic congestion and improve performance during peak periods in many cities. In our simulation model of Knoxville, TN we added tolls to certain roads in the network in order to acquire data that would help us distinguish whether changing tolls during peak hours would improve average travel time. Several sets of simulations were run and the data was recorded. It was concluded that changing the toll prices in general did not have a major impact on the average travel time and average travel distance. However, static and dynamic pricing structure comparisons showed that dynamic prices yielded lower average travel and stop times than static prices.Background and Description of the Problem SituationWhen driving down a toll road in any major city, people may wonder why they would pay money to drive on a specific road that may not actually get them to their destination any faster. Do toll road improve the average travel time of drivers in a traffic network? It seems as if toll road would help traffic spread out over more roads, as not all people are willing to pay tolls. If this is true, then adding tolls to certain major freeways should help average travel times decrease for drivers in the network, but is this really the case? We wanted to look at this specific problem. To answer this question, we acquired a simulation model of the traffic flow network of Knoxville, TN. We wanted to modify the simulation by adding tolls to roads in the network to see if this would change the average travel time for drivers in the -3-Shora - Tatschnetwork. In addition, we wanted to see if dynamic toll prices would have an impact on travel time through the network, as this is a new strategy being implemented around thecountry. By increasing toll prices during peak traffic hours, perhaps the average travel time would decrease as well. To set this problem up, we had to add some code to the simulation to enable us to place toll prices on certain arcs in the simulation. We had to decide which arcs to place tolls on, and what the standard prices should be. We also had to designate whichzones of people to make price sensitive versus insensitive and time sensitive versus insensitive. With toll prices applied to specific arcs, and groups of travelers given time and cost sensitivities, we were able to run the simulation and determine average travel times and distances. Analysis of the SituationWhen we initially acquired the simulation code and installed the necessary software need to run this program, we were unsure of how to solve the original problem we had in front of us. We met with our client to discuss how to begin solving the problem of dynamic toll prices and multiple organizations controlling these prices. We decided that this problem was too complicated and would require more modifications to the code. Time was also an important factor and we realized that realistically we had puttoo much on our shoulders. We settled on looking at the effect of a dynamic toll pricing strategy on the traffic network. We modified the code to allow us to determine arcs to apply tolls to. We ran the simulation many times with color-coded system to determine where the freeway arcs were on the simulation’s road map. After plotting these nodes and arcs, we picked several major arcs to turn in to toll roads. Next, we modified the code to essentially set a price for traveling on our designated arcs. After making different zones of people have different price and time sensitivities, we ran the simulation, modifying the toll prices each time and documented our results. We were required to make several assumptions in this problem, as this is a simulation. We assumed a reasonable toll price for a given arc would be $0.50, as mostof the toll roads in the Dallas area have a range of $0.45 to $0.75 for any particular segment of toll road traveled on. We also assumed that a company choosing what prices to set for its tolls would not have any huge jumps in dynamic pricing throughout the day, such as starting at $0.50 and then jumping to $3.00 for a given segment of road. We tried to gradually increase our prices during a given day. Another set of assumptions we made involved the travelers themselves. We needed to give different people different price and time sensitivities. To do this, we mapped out the nine zones of people that the simulation had set up. Next, we picked a -4-Shora - Tatschsocioeconomic status for residents in each zone based on similar areas in Dallas. For example, in the downtown area, we assumed the people to be wealthier, as in the Highland Park or Turtle Creek areas of Dallas. We assumed suburban areas were more upper-middle to middle class families. We also designated an area to be lower-income families in the inner city, etcetera. We assigned price sensitivities to these groups, assuming that upper-class families would be less sensitive to price than lower-income families. Likewise, we assumed that upper-class families tended to be more hurried and busy, and therefore made them more time sensitive than lower-income families. Technical Description of the ModelThe simulation model we used imitated the traffic flow of Knoxville, TN. This simulation has many components, but we only worked with a small fraction of the code. To set up the problem, we had to add or modify


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