The Capacity Model of Urban Multimode Transportation Network Based on Conbined Travel Mode

2021 ◽  
pp. 597-606
Author(s):  
Guozhu Cheng ◽  
Chuanmiao Zhou
Keyword(s):  
Transportation Network ◽  
Travel Mode ◽  
Capacity Model

scholarly journals Estimating the Capacity of Urban Transportation Networks with an Improved Sensitivity Based Method

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Muqing Du ◽  
Xiaowei Jiang ◽  
Lin Cheng
Keyword(s):  
Sensitivity Analysis ◽  
Large Scale ◽  
Transportation Network ◽  
Network Capacity ◽  
Combined Model ◽  
Practical Applications ◽  
Large Scale Problems ◽  
Capacity Model ◽  
High Efficient ◽  
Equilibrium Constraint

The throughput of a given transportation network is always of interest to the traffic administrative department, so as to evaluate the benefit of the transportation construction or expansion project before its implementation. The model of the transportation network capacity formulated as a mathematic programming with equilibrium constraint (MPEC) well defines this problem. For practical applications, a modified sensitivity analysis based (SAB) method is developed to estimate the solution of this bilevel model. The high-efficient origin-based (OB) algorithm is extended for the precise solution of the combined model which is integrated in the network capacity model. The sensitivity analysis approach is also modified to simplify the inversion of the Jacobian matrix in large-scale problems. The solution produced in every iteration of SAB is restrained to be feasible to guarantee the success of the heuristic search. From the numerical experiments, the accuracy of the derivatives for the linear approximation could significantly affect the converging of the SAB method. The results also show that the proposed method could obtain good suboptimal solutions from different starting points in the test examples.

scholarly journals Modeling the α-max capacity of transportation networks: a single-level mathematical programming formulation

2021 ◽  
Author(s):  
Zhaoqi Zang ◽  
Xiangdong Xu ◽  
Anthony Chen ◽  
Chao Yang
Keyword(s):  
Mathematical Programming ◽  
Travel Time ◽  
Large Scale ◽  
Transportation Network ◽  
Network Capacity ◽  
Solution Algorithm ◽  
Single Level ◽  
Capacity Model ◽  
Proposed Model ◽  
The Impact

AbstractNetwork capacity, defined as the largest sum of origin–destination (O–D) flows that can be accommodated by the network based on link performance function and traffic equilibrium assignment, is a critical indicator of network-wide performance assessment in transportation planning and management. The typical modeling rationale of estimating network capacity is to formulate it as a mathematical programming (MP), and there are two main approaches: single-level MP formulation and bi-level programming (BLP) formulation. Although single-level MP is readily solvable, it treats the transportation network as a physical network without considering level of service (LOS). Albeit BLP explicitly models the capacity and link LOS, solving BLP in large-scale networks is challenging due to its non-convexity. Moreover, the inconsideration of trip LOS makes the existing models difficult to differentiate network capacity under various traffic states and to capture the impact of emerging trip-oriented technologies. Therefore, this paper proposes the α-max capacity model to estimate the maximum network capacity under trip or O–D LOS requirement α. The proposed model improves the existing models on three aspects: (a) it considers trip LOS, which can flexibly estimate the network capacity ranging from zero to the physical capacity including reserve, practical and ultimate capacities; (b) trip LOS can intuitively reflect users’ maximum acceptable O–D travel time or planners’ requirement of O–D travel time; and (c) it is a convex and tractable single-level MP. For practical use, we develop a modified gradient projection solution algorithm with soft constraint technique, and provide methods to obtain discrete trip LOS and network capacity under representative traffic states. Numerical examples are presented to demonstrate the features of the proposed model as well as the solution algorithm.

scholarly journals Optimization Scheme of Tradable Credits and Bus Departure Quantity for Travelers’ Travel Mode Choice Guidance

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Fang Zhou ◽  
Jianhui Wu ◽  
Yan Xu ◽  
Chi Yi
Keyword(s):  
Road Traffic ◽  
Choice Model ◽  
Transportation Network ◽  
Mode Choice ◽  
Optimization Scheme ◽  
Travel Mode ◽  
Travel Mode Choice ◽  
Total Travel Time ◽  
Tradable Credits ◽  
Car Travel

To analyze the influence of tradable credits and bus departure quantity on travelers' travel mode choice, this study investigated car travel and bus travel as research objects and established a two-mode day-to-day travel mode choice model based on tradable credits and bus departure quantity. To improve the guiding effect of tradable credits and bus departure quantity, an optimization scheme of tradable credits and bus departure quantity was developed with the goal of minimizing the system total travel time of car travel and the system total comprehensive cost of bus travel. Taking a test transportation network as an example, the influence of no tradable credits scheme, tradable credits scheme, and tradable credits and bus departure quantity scheme on the travelers’ travel mode choice behavior was analyzed. The results showed that the tradable credits and bus departure quantity scheme could reduce the saturation of road traffic and improve bus service quality.

scholarly journals Price Regulation Mechanism of Travelers’ Travel Mode Choice in the Driverless Transportation Network

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jianhui Wu ◽  
Yuanfa Ji ◽  
Xiyan Sun ◽  
Yan Xu
Keyword(s):  
Transportation Network ◽  
Mode Choice ◽  
Price Regulation ◽  
Market Model ◽  
Regulation Mechanism ◽  
Optimal Price ◽  
Travel Mode ◽  
Travel Mode Choice ◽  
Calculation Process ◽  
Relationship Of

To study the guidance method of driverless travel mode choice from the perspective of traffic supply-demand, we assume that all vehicles are driverless and establish a multimodal travel market model to depict the supply-demand relationship of multimodal driverless transportation network. To regulate the disequilibrium multimodal travel market, an optimal price regulation law is proposed, which aims to minimize the supply-demand deviation and the amplitude of price regulation. Then, the existence, uniqueness, and stability of the optimal price regulation law are confirmed. In the calculation process of a numerical example, the travel prices of driverless car and driverless subway are realized by congestion fee and subway fare, respectively. The results indicate that the optimal price regulation law can reduce the supply-demand deviation of the multimodal travel market and guide travelers to choose a reasonable travel mode to travel in the driverless transportation network.

scholarly journals Typical Combined Travel Mode Choice Utility Model in Multimodal Transportation Network

2019 ◽  
Vol 11 (2) ◽  
pp. 549 ◽  
Author(s):  
Yue Liu ◽  
Jun Chen ◽  
Weiguang Wu ◽  
Jiao Ye
Keyword(s):  
Short Distance ◽  
Stated Preference ◽  
Personal Information ◽  
Transportation Network ◽  
Mode Choice ◽  
Influential Factors ◽  
Long Distance ◽  
Travel Mode ◽  
Travel Mode Choice ◽  
Walking Time

The primary purpose of this paper is to explore the mechanism of combined travel mode choice in multimodal networks. To meet the objective, stated preference survey and revealed preference survey are designed under short, middle, and long travel distance scenarios. Data including travelers’ socio-economic/personal information, trip characteristics, and mode choice are collected and analyzed. To recognize the influential factors of mode choice, a nested logit model is established. A value of time estimation and sensitivity analysis are conducted to quantify the influencing degree. The results reveal that cost has a significant influence on the short-distance travel mode; waiting time is perceived as the most important factor in short-distance scenario, and transfer-walking time as the most significant in middle and long distance scenario. Moreover, the traveler is more sensitive to the decrease of the transfer walking time than increase. Regarding socio-economic/personal information, travelers aged 40–50 prefer to choose combined travel mode than other ages; female travelers have a greater acceptance of metro-based transfer travel than male; individuals with higher economic level have a positive image of metro than bus.

scholarly journals MODELING AND ESTIMATING THE CAPACITY OF URBAN TRANSPORTATION NETWORK WITH RAPID TRANSIT

Transport ◽  
2014 ◽  
Vol 29 (2) ◽  
pp. 165-174 ◽  
Author(s):  
Lin Cheng ◽  
Muqing Du ◽  
Xiaowei Jiang ◽  
Hesham Rakha
Keyword(s):  
Travel Behavior ◽  
Travel Demand ◽  
Transportation Network ◽  
Urban Transportation ◽  
Network Capacity ◽  
Rapid Transit ◽  
Demand Model ◽  
Trip Distribution ◽  
Capacity Model ◽  
The Impact

To study the impact of the rapid transit on the capacity of current urban transportation system, a two-mode network capacity model, including the travel modes of automobile and transit, is developed based on the well-known road network capacity model. It considers that the travel demand accompanying with the regional development will increase in a variable manner on the trip distribution, of which the travel behavior is represented using the combined model split/trip distribution/traffic assignment model. Additionally, the choices of the travel routes, trip destinations and travel modes are formulated as a hierarchical logit model. Using this combined travel demand model in the lower level, the network capacity problem is formulated as a bi-level programming problem. The latest technique of sensitivity analysis is employed for the solution of the bi-level problem in a heuristic search. Numerical computations are demonstrated on an example network, and the before-and-after comparisons of building the new transit lines on the integrated transportation network are shown by the results.

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