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.

scholarly journals The Global Optimal Algorithm of Reliable Path Finding Problem Based on Backtracking Method

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Liang Shen ◽  
Hu Shao ◽  
Long Zhang ◽  
Jian Zhao
Keyword(s):  
Travel Time ◽  
Risk Taking ◽  
Optimal Algorithm ◽  
Optimal Path ◽  
Global Optimum ◽  
Path Finding ◽  
Shortest Path Algorithm ◽  
Global Optimal ◽  
Risk Taking Behaviors ◽  
Reliable Path

There is a growing interest in finding a global optimal path in transportation networks particularly when the network suffers from unexpected disturbance. This paper studies the problem of finding a global optimal path to guarantee a given probability of arriving on time in a network with uncertainty, in which the travel time is stochastic instead of deterministic. Traditional path finding methods based on least expected travel time cannot capture the network user’s risk-taking behaviors in path finding. To overcome such limitation, the reliable path finding algorithms have been proposed but the convergence of global optimum is seldom addressed in the literature. This paper integrates the K-shortest path algorithm into Backtracking method to propose a new path finding algorithm under uncertainty. The global optimum of the proposed method can be guaranteed. Numerical examples are conducted to demonstrate the correctness and efficiency of the proposed algorithm.

Vehicle Reidentification as Method for Deriving Travel Time and Travel Time Distributions: Investigation

2003 ◽  
Vol 1826 (1) ◽  
pp. 25-30 ◽  
Author(s):  
Carlos Sun ◽  
Glenn Arr ◽  
Ravi P. Ramachandran
Keyword(s):  
Travel Time ◽  
Time Distribution ◽  
Goodness Of Fit ◽  
Performance Test ◽  
Time Window ◽  
Time Windows ◽  
Travel Times ◽  
Travel Time Distribution ◽  
Vehicle Reidentification ◽  
And Control

Vehicle reidentification was investigated as a method for deriving travel time and travel time distributions with loop and video detectors. Vehicle reidentification is the process of tracking vehicles anonymously from site to site to produce individual vehicle travel times and overall travel time distribution. Travel time and travel time distribution are measures of the performance and reliability of the transportation system and are useful in many transportation applications such as planning, operations, and control. Findings from the investigation included ( a) results from a platoon reidentification algorithm that improved upon a previous indvidual vehicle reidentification algorithm, ( b) sensitivity analysis on the effect of time windows in deriving travel times, and ( c) derivation and goodness of fit of travel time distributions using vehicle reidentification. Arterial data from Southern California were used in testing the algorithm’s performance. Test results showed that the algorithm can reidentify vehicles with an accuracy of greater than 95.9% with 92.4% of total vehicles; can calculate individual travel times with approximately 1% mean error with the most effective time window; and can derive travel time distributions that fit actual distributions at a 99% confidence level.

scholarly journals Estimation of Urban Link Travel Time Distribution Using Markov Chains and Bayesian Approaches

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Wenwen Qin ◽  
Meiping Yun
Keyword(s):  
Markov Chain ◽  
Travel Time ◽  
Time Distribution ◽  
State Transition ◽  
Transition Function ◽  
Travel Times ◽  
Travel Time Distribution ◽  
Multimodal Distributions ◽  
State Transition Function ◽  
Link Travel Time

Despite the wide application of Floating Car Data (FCD) in urban link travel time and congestion estimation, the sparsity of observations from a low penetration rate of GPS-equipped floating cars make it difficult to estimate travel time distribution (TTD), especially when the travel times may have multimodal distributions that are associated with the underlying traffic states. In this case, the study develops a Bayesian approach based on particle filter framework for link TTD estimation using real-time and historical travel time observations from FCD. First, link travel times are classified by different traffic states according to the levels of vehicle delays. Then, a state-transition function is represented as a Transition Probability Matrix of the Markov chain between upstream and current links with historical observations. Using the state-transition function, an importance distribution is constructed as the summation of historical link TTDs conditional on states weighted by the current link state probabilities. Further, a sampling strategy is developed to address the sparsity problem of observations by selecting the particles with larger weights in terms of the importance distribution and a Gaussian likelihood function. Finally, the current link TTD can be reconstructed by a generic Markov Chain Monte Carlo algorithm incorporating high weighted particles. The proposed approach is evaluated using real-world FCD. The results indicate that the proposed approach provides good accurate estimations, which are very close to the empirical distributions. In addition, the approach with different percentage of floating cars is tested. The results are encouraging, even when multimodal distributions and very few or no observations exist.

scholarly journals Stability of Intelligent Transportation Network Dynamics: A Daily Path Flow Adjustment considering Travel Time Differentiation

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Ming-Chorng Hwang ◽  
Hsun-Jung Cho ◽  
You-Heng Huang
Keyword(s):  
Travel Time ◽  
Network Flow ◽  
Network Flows ◽  
Transportation Network ◽  
Stability Theorem ◽  
Flow Dynamics ◽  
Target System ◽  
Time Dynamics ◽  
Flow Adjustment ◽  
Induced Path

A theoretic formulation on how traffic time information distributed by ITS operations influences the trajectory of network flows is presented in this paper. The interactions between users and ITS operator are decomposed into three parts: (i) travel time induced path flow dynamics (PFDTT); (ii) demand induced path flow dynamics (PFDD); and (iii) predicted travel time dynamics for an origin-destination (OD) pair (PTTDOD). PFDTT describes the collective results of user’s daily route selection by pairwise comparison of path travel time provided by ITS services. The other two components, PTTDOD and PFDD, are concentrated on the evolutions of system variables which are predicted and observed, respectively, by ITS operators to act as a benchmark in guiding the target system towards an expected status faster. In addition to the delivered modelings, the stability theorem of the equilibrium solution in the sense of Lyapunov stability is also provided. A Lyapunov function is developed and employed to the proof of stability theorem to show the asymptotic behavior of the aimed system. The information of network flow dynamics plays a key role in traffic control policy-making. The evaluation of ITS-based strategies will not be reasonable without a well-established modeling of network flow evolutions.

Monitoring and Predicting Freeway Travel Time Reliability

2005 ◽  
Vol 1917 (1) ◽  
pp. 54-62 ◽  
Author(s):  
J. W. C. van Lint ◽  
H. J. van Zuylen
Keyword(s):  
Travel Time ◽  
Discrete Choice Models ◽  
Time Of Day ◽  
Travel Time Reliability ◽  
Travel Times ◽  
Travel Time Prediction ◽  
Explanatory Variables ◽  
Travel Time Distribution ◽  
Mean And Variance ◽  
Context Specific

Generally, the day-to-day variability of route travel times on, for example, freeway corridors is considered closely related to the reliability of a road network. The more that travel times on route r are dispersed in a particular time-of-day (TOD) and day-of-week (DOW) period, the more unreliable travel times on route r are conceived to be. In the literature, many different aspects of the day-to-day travel time distribution have been proposed as indicators of reliability. Mean and variance do not provide much insight because those metrics tend to obscure important aspects of the distribution under specific circumstances. It is argued that both skew and width of this distribution are relevant indicators for unreliability; therefore, two reliability metrics are proposed. These metrics are based on three characteristic percentiles: the 10th, 50th, and 90th percentile for a given route and TOD-DOW period. High values of either metric indicate high travel time unreliability. However, the weight of each metric on travel time reliability may be application- or context-specific. The practical value of these particular metrics is that they can be used to construct so-called reliability maps, which not only visualize the unreliability of travel times for a given DOW-TOD period but also help identify DOW-TOD periods in which congestion will likely set in (or dissolve). That means identification of the uncertainty of start, end, and, hence, length of morning and afternoon peak hours. Combined with a long-term travel time prediction model, the metrics can be used to predict travel time (un)reliability. Finally, the metrics may be used in discrete choice models as explanatory variables for driver uncertainty.

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.

scholarly journals Expansion and contraction of the flowing stream network changes hillslope flowpath lengths and the shape of the travel time distribution

2019 ◽  
Author(s):  
H. J. Ilja van Meerveld ◽  
James W. Kirchner ◽  
Marc J. P. Vis ◽  
Rick S. Assendelft ◽  
Jan Seibert
Keyword(s):  
Travel Time ◽  
Time Distribution ◽  
Network Dynamics ◽  
Subsurface Flow ◽  
Travel Times ◽  
Stream Networks ◽  
Stream Network ◽  
Transport Distance ◽  
Travel Time Distribution ◽  
Flowing Stream

Abstract. Flowing stream networks dynamically extend and retract, both seasonally and in response to precipitation events. These network dynamics can dramatically alter the drainage density, and thus the length of subsurface flow pathways to flowing streams. We mapped flowing stream networks in a small Swiss headwater catchment during different wetness conditions and estimated their effects on the distribution of travel times to the catchment outlet. For each point in the catchment, we determined the subsurface transport distance to the flowing stream based on the surface topography, and the surface transport distance along the flowing stream to the outlet. We combined the distributions of these travel distances with assumed surface and subsurface flow velocities to estimate the distribution of travel times to the outlet. These calculations show that the extension and retraction of the stream network can substantially change the mean travel time and the shape of the travel time distribution. During wet conditions with a fully extended flowing stream network, the travel time distribution was strongly skewed to short travel times, but as the network retracted during dry conditions, the distribution of the travel times became more uniform. Stream network dynamics are widely ignored in catchment models, but our results show that they need to be taken into account when modeling solute transport and interpreting travel time distributions.

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.

scholarly journals MODELLING TRAVEL TIME DISTRIBUTION AND ITS INFLUENCE OVER STOCHASTIC VEHICLE SCHEDULING

Transport ◽  
2019 ◽  
Vol 34 (3) ◽  
pp. 237-249
Author(s):  
Yindong Shen ◽  
Jia Xu ◽  
Xianyi Wu ◽  
Yudong Ni
Keyword(s):  
Travel Time ◽  
Service Planning ◽  
Vehicle Scheduling ◽  
Travel Times ◽  
Distribution Fitting ◽  
Distribution Models ◽  
Time Model ◽  
Travel Time Distribution ◽  
Compound Model ◽  
Research Findings

Due to the paucity of well-established modelling approaches or well-accepted travel time distributions, the existing travel time models are often assumed to follow certain popular distributions, such as normal or lognormal, which may lead to results deviating from actual ones. This paper proposes a modelling approach for travel times using distribution fitting methods based on the data collected by Automatic Vehicle Location (AVL) systems. By this proposed approach, a compound travel time model can be built, which consists of the best distribution models for the travel times in each period of a day. Applying to stochastic vehicle scheduling, the influence of different travel time models is further studied. Results show that the compound model can fit more precisely to the actual travel times under various traffic situations, whilst the on-time performance of resulting vehicle schedules can be improved. The research findings have also potential benefit for the other research based on travel time models in public transport including timetabling, service planning and reliability measurement.

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