Evaluating Effects of Toll Strategies on Route Diversion and Travel Times for Origin–Destination Pairs in a Regional Transportation Network

2007 ◽  
Vol 2035 (1) ◽  
pp. 205-215 ◽  
Author(s):  
Haitham Al-Deek ◽  
Srinivasa Ravi Chandra ◽  
Emam B. Emam ◽  
Jack Klodzinski
Keyword(s):  
Transportation Network ◽  
Travel Times ◽  
Route Diversion

scholarly journals Network Fundamental Diagram Based Routing of Vehicle Fleets in Dynamic Traffic Simulations

2020 ◽  
Author(s):  
Florian Dandl ◽  
Gabriel Tilg ◽  
Majid Rostami-Shahrbabaki ◽  
Klaus Bogenberger
Keyword(s):  
Travel Time ◽  
Transportation Network ◽  
Time Factor ◽  
Travel Times ◽  
Dynamic Traffic ◽  
Fundamental Diagram ◽  
Trade Off ◽  
On Demand ◽  
Network Fundamental Diagram ◽  
The Impact

The growing popularity of mobility-on-demand fleets increases the importance to understand the impact of mobility-on-demand fleets on transportation networks and how to regulate them. For this purpose, transportation network simulations are required to contain corresponding routing methods. We study the trade-off between computational efficiency and routing accuracy of different approaches to routing fleets in a dynamic network simulation with endogenous edge travel times: a computationally cheap but less accurate Network Fundamental Diagram (NFD) based method and a more typical Dynamic Traffic Assignment (DTA) based method. The NFD-based approach models network dynamics with a network travel time factor that is determined by the current average network speed and scales free-flow travel times. We analyze the different computational costs of the approaches in a case study for 10,000 origin-destination (OD) pairs in a network of the city of Munich, Germany that reveals speedup factors in the range of 100. The trade-off for this is less accurate travel time estimations for individual OD pairs. Results indicate that the NFD-based approach overestimates the DTA-based travel times, especially when the network is congested. Adjusting the network travel time factor based on pre-processed DTA results, the NFD-based routing approach represents a computationally very efficient methodology that also captures traffic dynamics in an aggregated way.

Factors Affecting Minimum Number of Probes Required for Reliable Estimation of Travel Time

2005 ◽  
Vol 1917 (1) ◽  
pp. 37-44 ◽  
Author(s):  
Mecit Cetin ◽  
George F. List ◽  
Yingjie Zhou
Keyword(s):  
Travel Time ◽  
Transportation Network ◽  
Travel Time Reliability ◽  
System State ◽  
Travel Times ◽  
State Estimate ◽  
Factors Affecting ◽  
Minimum Number ◽  
Time Information ◽  
Detection Technologies

Using probe vehicles rather than other detection technologies has great value, especially when travel time information is sought in a transportation network. Even though probes enable direct measurement of travel times across links, the quality or reliability of a system state estimate based on such measurements depends heavily on the number of probe observations across time and space. Clearly, it is important to know what level of travel time reliability can be achieved from a given number of probes. It is equally important to find ways (other than increasing the sample size of probes) of improving the reliability in the travel time estimate. This paper provides two new perspectives on those topics. First, the probe estimation problem is formulated in the context of estimating travel times. Second, a method is introduced to create a virtual network by inserting dummy nodes in the midpoints of links to enhance the ability to estimate travel times further in a way that is more consistent with the processing that vehicles receive. Numerical experiments are presented to illustrate the value of those ideas.

scholarly journals Network Fundamental Diagram Based Routing of Vehicle Fleets in Dynamic Traffic Simulations

2020 ◽  
Author(s):  
Florian Dandl ◽  
Gabriel Tilg ◽  
Majid Rostami-Shahrbabaki ◽  
Klaus Bogenberger
Keyword(s):  
Travel Time ◽  
Transportation Network ◽  
Time Factor ◽  
Travel Times ◽  
Dynamic Traffic ◽  
Fundamental Diagram ◽  
Trade Off ◽  
On Demand ◽  
Network Fundamental Diagram ◽  
The Impact

The growing popularity of mobility-on-demand fleets increases the importance to understand the impact of mobility-on-demand fleets on transportation networks and how to regulate them. For this purpose, transportation network simulations are required to contain corresponding routing methods. We study the trade-off between computational efficiency and routing accuracy of different approaches to routing fleets in a dynamic network simulation with endogenous edge travel times: a computationally cheap but less accurate Network Fundamental Diagram (NFD) based method and a more typical Dynamic Traffic Assignment (DTA) based method. The NFD-based approach models network dynamics with a network travel time factor that is determined by the current average network speed and scales free-flow travel times. We analyze the different computational costs of the approaches in a case study for 10,000 origin-destination (OD) pairs in a network of the city of Munich, Germany that reveals speedup factors in the range of 100. The trade-off for this is less accurate travel time estimations for individual OD pairs. Results indicate that the NFD-based approach overestimates the DTA-based travel times, especially when the network is congested. Adjusting the network travel time factor based on pre-processed DTA results, the NFD-based routing approach represents a computationally very efficient methodology that also captures traffic dynamics in an aggregated way.

Travel Time Variability and Congestion Assessment for Origin–Destination Clusters through the Experience of Mobility Companies

2020 ◽  
Vol 2674 (12) ◽  
pp. 103-117
Author(s):  
Haleh Ale-Ahmad ◽  
Ying Chen ◽  
Hani S. Mahmassani
Keyword(s):  
Travel Time ◽  
Network Performance ◽  
Transportation Network ◽  
Time Of Day ◽  
Time Variability ◽  
Travel Times ◽  
Time Data ◽  
Unit Distance ◽  
Reliability Performance ◽  
Network Approaches

Reliability is a measure of network performance that reflects the ability of the network to provide predictable travel times. Deviations from planned travel times can increase travel costs for users. To improve the system's performance, it is crucial to identify sources of unreliability, particularly the location on the network of unreliable performance. The large amount of travel time data recorded by Transportation Network Providers (TNPs) in recent years has enabled researchers to study the performance of entire networks. In this study, a real-world dataset provided by TNPs in Chicago is used to determine time of day, and day of week distribution of travel time per unit distance for origin–destination (OD) pairs. Eight measures of reliability are calculated for OD pairs in the network. Standard deviation (SD), planning time index (PTI), and on-time measure (PR) are used for a network-wide comparison of reliability performance. K-means clustering is performed on more than 21.3 million trips to divide 3,450 eligible OD pairs in the Chicago network into three groups with low, medium, and high intensity of each reliability metric. Lastly, metrics in each cluster of SD, PTI, and PR are compared. The results show that ranking PTI and PR is not sufficient for identifying unreliable/congested OD pairs in the network. Approaches for comparing reliability performance over different periods of the day for the same segment and over different segments in the network are discussed, along with network-wide measures of reliability.

Quantifying the Criticality of Highway Infrastructure for Freight Transportation

2017 ◽  
Vol 2610 (1) ◽  
pp. 10-18 ◽  
Author(s):  
Zahra Ashrafi ◽  
Hamed Shahrokhi Shahraki ◽  
Chris Bachmann ◽  
Kevin Gingerich ◽  
Hanna Maoh
Keyword(s):  
Cost Benefit Analysis ◽  
Transportation Network ◽  
Cost Benefit ◽  
Freight Transportation ◽  
Weather Conditions ◽  
Travel Times ◽  
Economic Productivity ◽  
Highway Infrastructure ◽  
Greater Toronto Area ◽  
The Cost

Events that disable parts of the highway transportation network, ranging from weather conditions to construction closures, may affect freight travel times and ultimately degrade economic productivity. Although previous studies of criticality typically focused on the impacts of natural disasters or terrorist attacks on systemwide travel times, these studies did not quantify the costs associated with disruptions to the economy because of disruptions to the freight transportation system. This paper quantifies the economic criticality of the highway infrastructure in Ontario, Canada, with the use of a new measure of criticality that determines the cost of highway closures (in dollars) on the basis of the value of goods, the time delayed, and the associated value of time. When criticality is measured in this way, it has some correlation with truck volumes, but the correlation differs when the values of shipments and the physical redundancy in the network are considered, and results in new insights into critical freight infrastructure. For example, the highway network within the greater Toronto, Ontario, Canada, area has a high degree of redundancy, but highways farther away from this metropolitan area have less redundancy and are thus more critical. Moreover, sections of Highway 401 located west of the greater Toronto area were found to be more critical—even though it carries lower truck volumes—than those located east of the greater Toronto area because of the lower redundancy in the western portion of the network. This measure has many potential applications in freight transportation planning, operations, and maintenance. Finally, with the cost of these disruptions quantified in dollars, one can then calculate the monetary benefits of potential transportation improvements for comparison (i.e., perform a cost–benefit analysis).

Stochastic Multiclass Traffic Assignment with Consideration of Risk-Taking Behaviors

2008 ◽  
Vol 2085 (1) ◽  
pp. 111-123 ◽  
Author(s):  
Shan Di ◽  
Changxuan Pan ◽  
Bin Ran
Keyword(s):  
Travel Time ◽  
Risk Taking ◽  
Network Flows ◽  
Simulation Analysis ◽  
Transportation Network ◽  
Travel Times ◽  
Traffic Equilibrium ◽  
Travel Time Distribution ◽  
Log Normal ◽  
Risk Taking Behaviors

A study of the problem of predicting traffic flows under traffic equilibrium in a stochastic transportation network is presented. Travelers’ risk-taking behaviors are explicitly modeled with respect to probabilistic travel times. Traveling risks are quantified from the travel time distributions directly and are embedded in the route choice conditions. The classification of risk-neutral, risk-averse, and risk-prone travelers is based on their preferred traveling risks. The formulation of the model clarifies that travelers with different risk preferences have the same objective–to save travel time cost–though they may make different route choices. The proposed solution algorithm is applicable for networks with normal distribution link travel times theoretically. Further simulation analysis shows that it can also be applied to approximate the equilibrium network flows for other frequently used travel time distribution families: gamma, Weibull, and log-normal. The proposed model was applied to a test network and a medium-sized transportation network. The results demonstrate that the model captures travelers’ risk-taking behaviors more realistically and flexibly compared with existing stochastic traffic equilibrium models.

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