scholarly journals A Modified Network-Wide Road Capacity Reliability Analysis Model for Improving Transportation Sustainability

Algorithms ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 7
Author(s):  
Kui Ji ◽  
Jianxiao Ma
Keyword(s):  
Reliability Analysis ◽  
Travel Time ◽  
Road Network ◽  
Programming Model ◽  
Network Capacity ◽  
User Equilibrium ◽  
Stochastic User Equilibrium ◽  
Analysis Model ◽  
Step Size ◽  
Level Model

Reserve capacity is the core of reliability analysis based on road network capacity. This article optimizes its bi-level programming model: the upper-level model has added service level coefficient and travel time limit, which realizes the unification of capacity and travel time reliability analysis to a certain extent. Stochastic user equilibrium (SUE) model based on origin has been selected as the lower-level model, which added capacity constraints to optimize distribution results, and allows the evaluation of reliability to be continued. Through the SUE model, the iterative step size of the method of successive averages can be optimized to improve the efficiency of the algorithm. After that, the article designs the algorithm flow of reliability analysis based on road network capacity, and verifies the feasibility of the designed algorithm with an example. Finally, combined with the conclusion of reliability analysis, this article puts forward some effective methods to improve the reliability of the urban road network.

scholarly journals A Multistage Multiobjective Model for Emergency Evacuation Considering ATIS

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Ming-Hua Zeng ◽  
Ke-Jun Long ◽  
Zi-Wen Ling ◽  
Xi-Yan Huang
Keyword(s):  
Travel Time ◽  
Road Network ◽  
Network Performance ◽  
Programming Model ◽  
Choice Model ◽  
Compliance Rate ◽  
Emergency Evacuation ◽  
User Equilibrium ◽  
Stochastic User Equilibrium ◽  
Traffic Demand

The impacts of advanced traveler information system’s (ATIS’s) penetration and compliance rates on network performances during hybrid traffic emergency evacuation are investigated in a degraded road network. Before traffic incident a Path-Size Logit (PSL) route choice model is integrated with constraints on the level of service (LOS) of traffic to formulate a bilevel programming model. It aims at minimizing traffic demand in road network which may locally deteriorate the LOS. The lower level is a PSL-stochastic user equilibrium model for multiple classes of users. During the ongoing incident, a multiobjective multiuser-class stochastic optimization model is established with the objectives of maximizing evacuation reliability and minimizing expected network travel time. Furthermore, computations and analyses are completed for five designated scenarios including a method proposed in previous literature. The results show that the evacuation reliability and different kinds of total expected travel time costs regularly increase with emergency traffic’s ATIS compliance rate and decrease with general traffic’s ATIS penetration rate. The research will help improve transport network performance when considering ATIS’s effect on hybrid traffic.

scholarly journals Continuous Network Design Based on the Paired Combinatorial Logit Stochastic User Equilibrium Model

2014 ◽  
Vol 8 (1) ◽  
pp. 316-322
Author(s):  
Xuefei Li ◽  
Maoxiang Lang
Keyword(s):  
Network Design ◽  
Objective Function ◽  
Equilibrium Model ◽  
Design Problem ◽  
Programming Model ◽  
User Equilibrium ◽  
Network Design Problem ◽  
Stochastic User Equilibrium ◽  
Level Model ◽  
The Impact

In order to design the traffic network more accurately, the bi-level programming model for the continuous network design problem based on the paired combinatorial Logit stochastic user equilibrium model is proposed in this study. In the model, the paired combinatorial Logit stochastic user equilibrium model which is used to characterize the route choice behaviors of the users is adopted in the lower level model, and the minimum summation of the system total costs and investment amounts is used in the upper objective function. The route-based self-regulated averaging (SRA) algorithm is designed to solve the stochastic user equilibrium model and the genetic algorithm (GA) is designed to get the optimal solution of the upper objective function. The effectiveness of the proposed combining algorithm which contains GA and SRA is verified by using a simple numerical example. The solutions of the bi-level models which use the paired combinatorial Logit stochastic user equilibrium model in the lower level model with different demand levels are compared. Finally, the impact of the dispersion coefficient parameter which influences the decision results of the network design problem is analyzed.

scholarly journals BI-LEVEL PROGRAMMING MODEL AND ALGORITHMS FOR STOCHASTIC NETWORK WITH ELASTIC DEMAND

Transport ◽  
2015 ◽  
Vol 30 (1) ◽  
pp. 117-128 ◽  
Author(s):  
Xiang Zhang ◽  
Hao Wang ◽  
Wei Wang
Keyword(s):  
Travel Demand ◽  
Programming Model ◽  
Choice Model ◽  
Consumer Surplus ◽  
User Equilibrium ◽  
Network Design Problem ◽  
Elastic Demand ◽  
Stochastic User Equilibrium ◽  
Proposed Model ◽  
Level Model

Based on a state-of-the-art review of the Road Network Design Problem (RNDP), this paper proposes a bi-level programming model for the RNDP as well as algorithms for it. In the lower level of the proposed model, the elastic-demand Stochastic User Equilibrium (SUE) model is adopted to coincide well with characteristics of users behavior, and additionally, the parameter calibration method for the model is developed based on the Logit path choice model. In the upper level of the proposed model, the consumer surplus is maximized to improve the social benefit of a network in consideration of the travel demand, the construction cost, the off-gas emissions and the saturation level. The algorithm for the lower-level model is developed based on the descent iteration method, Dijkstra’s algorithm and linear search technology. A modified Genetic Algorithm (GA) is developed as the algorithm for the whole bi-level model, which takes designed elitist selection operator, adaptive cross operator, mutation operator and niche technology into consideration. The proposed model and algorithms are applied to a numerical example. The results demonstrate the validity and efficiency of the model and algorithms, which shows a bright prospect of the application in RNDP.

scholarly journals Design of Signal Timing Plan for Urban Signalized Networks including Left Turn Prohibition

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Qinrui Tang ◽  
Bernhard Friedrich
Keyword(s):  
Travel Time ◽  
Treatment Options ◽  
User Equilibrium ◽  
Signal Timing ◽  
Stochastic User Equilibrium ◽  
Left Turn ◽  
Traffic Demand ◽  
Total Travel Time ◽  
Left Turns ◽  
Demand Patterns

Urban road networks may benefit from left turn prohibition at signalized intersections regarding capacity, for particular traffic demand patterns. The objective of this paper is to propose a method for minimizing the total travel time by prohibiting left turns at intersections. With the flows obtained from the stochastic user equilibrium model, we were able to derive the stage generation, stage sequence, cycle length, and the green durations using a stage-based method which can handle the case that stages are sharing movements. The final output is a list of the prohibited left turns in the network and a new signal timing plan for every intersection. The optimal list of prohibited left turns was found using a genetic algorithm, and a combination of several algorithms was employed for the signal timing plan. The results show that left turn prohibition may lead to travel time reduction. Therefore, when designing a signal timing plan, left turn prohibition should be considered on a par with other left turn treatment options.

Privacy Implication of Location-Based Service: Multi-Class Stochastic User Equilibrium and Incentive Mechanism

2019 ◽  
Vol 2673 (12) ◽  
pp. 256-265
Author(s):  
Haitao Hu ◽  
Zhanbo Sun ◽  
Runzhe Liu ◽  
Xia Yang
Keyword(s):  
Travel Time ◽  
Incentive Mechanism ◽  
Time Estimation ◽  
User Equilibrium ◽  
Travel Time Estimation ◽  
Stochastic User Equilibrium ◽  
Location Based Service ◽  
Modeling Framework ◽  
Privacy Concerns ◽  
Traffic Guidance

As a tool to assist traffic guidance and improve service quality, location-based service (LBS) platforms such as route navigation apps rely heavily on the collection and analysis of users’ location/trajectory information, which may evoke privacy concerns. Because of such privacy concerns, users may choose not to provide their information. In certain cases, this may lead to the problem of insufficient data for LBS applications (e.g., travel time estimation). To address this issue, the paper develops a modeling framework to quantify the levels of privacy for mixed user groups and proposes an incentive mechanism to encourage users to provide their location/trajectory information. It is assumed that LBS users have smaller travel time perception error but experience some extra privacy costs compared with the non-LBS users. A bi-level optimal incentive model with stochastic user equilibrium and elastic demand is developed to capture the mixed behavior of multi-class network users. The problem is solved using a meta-heuristic approach combined of genetic algorithm, successive average algorithm, and multiple behavior equilibrium assignment algorithm. The results reveal that the modeling framework can capture the mixed behavior of groups with different privacy levels. The proposed incentive mechanism is able to ensure sufficient data, and simultaneously minimize the required incentive.

scholarly journals A Simultaneous Solution for Reserve Capacity Maximization and Delay Minimization Problems in Signalized Road Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Ozgur Baskan ◽  
Huseyin Ceylan ◽  
Cenk Ozan
Keyword(s):  
Bilevel Programming ◽  
Road Network ◽  
Programming Model ◽  
Numerical Test ◽  
User Equilibrium ◽  
Reserve Capacity ◽  
Delay Minimization ◽  
Weighted Sum Method ◽  
Capacity Maximization ◽  
Bilevel Programming Model

In this study, we present a bilevel programming model in which upper level is defined as a biobjective problem and the lower level is considered as a stochastic user equilibrium assignment problem. It is clear that the biobjective problem has two objectives: the first maximizes the reserve capacity whereas the second minimizes performance index of a road network. We use a weighted-sum method to determine the Pareto optimal solutions of the biobjective problem by applying normalization approach for making the objective functions dimensionless. Following, a differential evolution based heuristic solution algorithm is introduced to overcome the problem presented by use of biobjective bilevel programming model. The first numerical test is conducted on two-junction network in order to represent the effect of the weighting on the solution of combined reserve capacity maximization and delay minimization problem. Allsop & Charlesworth’s network, which is a widely preferred road network in the literature, is selected for the second numerical application in order to present the applicability of the proposed model on a medium-sized signalized road network. Results support authorities who should usually make a choice between two conflicting issues, namely, reserve capacity maximization and delay minimization.

Estimation of Origin-Destination Matrix with Tolling Data

2011 ◽  
Vol 50-51 ◽  
pp. 239-244 ◽  
Author(s):  
Hua Wang ◽  
Xiao Ning Zhang
Keyword(s):  
Simulated Annealing ◽  
Travel Time ◽  
Road Network ◽  
Programming Model ◽  
Heuristic Method ◽  
Traffic Information ◽  
Traditional Model ◽  
Matrix Estimation ◽  
Od Estimation ◽  
The Cost

Prior matrix and surveyed link volumes were, in most cases, employed to estimate origin-destination matrix. With the development of BOT and of congestion pricing, charged links become an important component of road network, due to the fact that the tolling data: volumes and travel time on pricing entry-exit are traffic information, both cost-free and accurate. In this paper, we put forward a bi-level programming model, taking account of data on charging entry-exit to estimate OD matrix based upon the traditional model. Meanwhile, a heuristic method -the simulated annealing approach - is utilized to solve the OD estimation problem. Results of examples indicate that the accuracy of estimation will be improved while adding the tolling data, and that it is feasible to calculate OD matrix by combining the volumes and travel time on entry-exit with partial common link flows. In this light, this way can be applied to enhance accuracy, and also to reduce the cost spent on surveying the link flows in common OD matrix estimation.

scholarly journals Reliability of Travel Time in a Network through Stochastic User Equilibrium

1998 ◽  
Vol 15 ◽  
pp. 647-654
Author(s):  
Hiroshi INOUYE ◽  
Yuzo IIDA ◽  
Keiichi KISHINO ◽  
Tetsuro HASEGAWA
Keyword(s):  
Travel Time ◽  
User Equilibrium ◽  
Stochastic User Equilibrium

Stochastic Traffic Assignment Model Based on Travel Time Reliability

2011 ◽  
Vol 130-134 ◽  
pp. 3716-3720
Author(s):  
Yi Ran Cheng ◽  
Yin Han ◽  
Xin Kai Jiang ◽  
Jia Lei Gu
Keyword(s):  
Travel Time ◽  
Equilibrium Model ◽  
Choice Behavior ◽  
Transportation Networks ◽  
Route Choice ◽  
User Equilibrium ◽  
Travel Time Reliability ◽  
Stochastic User Equilibrium ◽  
Model Based ◽  
Route Choice Behavior

Considering the un-deterministic transportation networks, the paper proposes the change of the route choice decisions under the stochastic transportation networks. The route choice behavior is described as a choice for a time shortest route which is subject to a time-reliability level. The paper also considered this new route choice behavior in the stochastic user equilibrium model, and proposed stochastic user equilibrium model based on the optimized reliability travel time route choice behavior in the stochastic networks. The equivalence and uniqueness of the solution of the model are demonstrated. Numerical results of a small network show that the proposed model can reflect the real traveler’s route choice behavior in stochastic transportation networks.

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