scholarly journals Analytical Model for Travel Time-Based BPR Function with Demand Fluctuation and Capacity Degradation

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Junjie Zhang ◽  
Miaomiao Liu ◽  
Bin Zhou
Keyword(s):  
Travel Time ◽  
Transportation Network ◽  
Empirical Studies ◽  
Network Capacity ◽  
Stochastic Network ◽  
Traffic Demand ◽  
Proposed Model ◽  
Capacity Degradation ◽  
Sensitivity Analysis Method ◽  
Demand Sensitivity

This study presents a stochastic model based on the link performance function of the Bureau of Public Roads to assess the reliability of travel time in the transportation network. Empirical studies have verified that the variability of travel time can be ascribed to demand fluctuation and the degradation of the capacity of the stochastic network. The mean-variance approach in previous research presented the budget model of travel time, with the capacity of the stochastic network and elastic demand as the sources of uncertainty of travel time. Previous research was devoted to the study of estimation of travel time considering a single factor or a factor independent of these two sources. Meanwhile, this study introduces the current degeneration coefficient of capacity (CDC) and the density distribution function of road section saturation (DDFS) with simultaneous network capacity and traffic demand. Sensitivity analysis method for the parameters of the proposed model is investigated theoretically using the sensitivity model of traffic capacity degradation. Results of case analysis show that the DDFS and CDC have an effect on the decision of travelers regarding the choice of route. The empirical analysis also illustrates the effectiveness of the computational approach and the proposed model.

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.

Definition and Evaluation Modeling of Transportation Network Layout Adaptability

2011 ◽  
Vol 71-78 ◽  
pp. 3938-3941 ◽  
Author(s):  
Jie Gao ◽  
Mei Xiang Wu ◽  
Chen Qiang Yin
Keyword(s):  
Heuristic Algorithm ◽  
Coefficient Of Variation ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Optimization Methods ◽  
Network Capacity ◽  
Network Layout ◽  
Proposed Model ◽  
Coordination Degree

According to the reliability theories and the characteristics of transportation networks, the layout adaptability is defined as the coupling and coordination degree of transportation network capacity and demand firstly. Then a layout adaptability model is built adopting the optimization methods, degree of layout adaptability index and coefficient of variation are used to evaluate the adaptability of scale and distribution respectively. Meanwhile, the heuristic algorithm suitable for large scale is designed to solve the proposed model. At last, a numerical example and its results are provided to demonstrate the validity of the proposed model and algorithm.

scholarly journals Flexible Bus Route Optimization for Multitarget Stations

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Sun Ji-yang ◽  
Huang Jian-ling ◽  
Chen Yan-yan ◽  
Wei Pan-yi ◽  
Jia Jian-lin
Keyword(s):  
Travel Time ◽  
Optimization Model ◽  
Transportation Network ◽  
Transportation Systems ◽  
Route Optimization ◽  
Planning Time ◽  
Bus Scheduling ◽  
Proposed Model ◽  
Average Travel Time ◽  
Better Than

This paper proposes a flexible bus route optimization model for efficient public city transportation systems based on multitarget stations. The model considers passenger demands, vehicle capacities, and transportation network and aims to solve the optimal route, minimizing the vehicles’ running time and the passengers’ travel time. A heuristic algorithm based on a gravity model is introduced to solve this NP-hard optimization problem. Simulation studies verify the effectiveness and practicality of the proposed model and algorithm. The results show that the total number of vehicles needed to complete the service is 17–21, the average travel time of each vehicle is 24.59 minutes, the solving time of 100 sets of data is within 25 seconds, and the average calculation time is 12.04 seconds. It can be seen that under the premise of real-time adjustment of connection planning time, the optimization model can satisfy the passenger’s dynamic demand to a greater extent, and effectively reduce the planning path error, shorten the distance and travel time of passengers, and the result is better than that of the flexible bus scheduling model which ignores the change of connection travel time.

scholarly journals Public transport route planning in the stochastic network based on the user individual preferences

2019 ◽  
pp. 66-71
Author(s):  
A A Borodinov ◽  
A S Yumaganov ◽  
A A Agafonov
Keyword(s):  
Travel Time ◽  
Public Transport ◽  
Transportation Network ◽  
Route Planning ◽  
Transport Systems ◽  
Stochastic Network ◽  
Individual Preferences ◽  
Dijkstra's Algorithm ◽  
Starting Point ◽  
Planning Algorithm

Nowadays transport systems becomes more and more complex. Therefore, passengers have difficulty with route planning due to the variety of possible ways to get from the starting point to the destination one. Since the travel time often not considered as single and main criteria by passengers, it is important to take into account their own preferences which may be very different. In this paper, we proposed a stochastic route planning algorithm, which considers the user individual preferences. This method is based on the modified Dijkstra’s algorithm. The proposed algorithm is tested using real public transport dataset obtained from the transportation network of Samara, Russia.

Equilibrium Assignment Model with Uncertainties in Traffic Demands

2012 ◽  
Vol 253-255 ◽  
pp. 1751-1757
Author(s):  
Ai Wu Kuang ◽  
Zhong Xiang Huang
Keyword(s):  
Travel Time ◽  
Travel Times ◽  
Heuristic Solution ◽  
Traffic Demand ◽  
Assignment Model ◽  
Proposed Model ◽  
Test Road ◽  
Small Test ◽  
Log Normal ◽  
Past Experiences

In this paper, we present an equilibrium traffic assignment model considering uncertainties in traffic demands. The link and route travel time distributions are derived based on the assumption that OD traffic demand follows a log-normal distribution. We postulate that travelers can acquire the variability of route travel times from past experiences and factor such variability into their route choice considerations in the form of mean route travel time. Furthermore, all travelers want to minimize their mean route travel times. We formulate the assignment problem as a variational inequality, which can be solved by a route-based heuristic solution algorithm. Some numerical studies on a small test road network are carried out to validate the proposed model and algorithm, at the same time, some reasonable results are obtained.

scholarly journals Economical Speed for Optimizing the Travel Time and Energy Consumption in Train Scheduling using a Fuzzy Multi-Objective Model

2021 ◽  
Author(s):  
Ahmad Reza Jafarian-Moghaddam
Keyword(s):  
Energy Consumption ◽  
Travel Time ◽  
Pollutant Emissions ◽  
Scheduling Problems ◽  
Train Scheduling ◽  
Multi Objective ◽  
Consumption Ratio ◽  
Proposed Model ◽  
Objective Model ◽  
Time And Energy

AbstractSpeed is one of the most influential variables in both energy consumption and train scheduling problems. Increasing speed guarantees punctuality, thereby improving railroad capacity and railway stakeholders’ satisfaction and revenues. However, a rise in speed leads to more energy consumption, costs, and thus, more pollutant emissions. Therefore, determining an economic speed, which requires a trade-off between the user’s expectations and the capabilities of the railway system in providing tractive forces to overcome the running resistance due to rail route and moving conditions, is a critical challenge in railway studies. This paper proposes a new fuzzy multi-objective model, which, by integrating micro and macro levels and determining the economical speed for trains in block sections, can optimize train travel time and energy consumption. Implementing the proposed model in a real case with different scenarios for train scheduling reveals that this model can enhance the total travel time by 19% without changing the energy consumption ratio. The proposed model has little need for input from experts’ opinions to determine the rates and parameters.

Effects of Connectivity and Traffic Observability on an Adaptive Traffic Signal Control System

2021 ◽  
pp. 036119812110130
Author(s):  
S M A Bin Al Islam ◽  
Mehrdad Tajalli ◽  
Rasool Mohebifard ◽  
Ali Hajbabaie
Keyword(s):  
Travel Time ◽  
Market Share ◽  
Signal Control ◽  
Market Penetration ◽  
Travel Time Reliability ◽  
Traffic Demand ◽  
Adaptive Signal Control ◽  
Loop Detector ◽  
Traffic Performance ◽  
Adaptive Signal

The effectiveness of adaptive signal control strategies depends on the level of traffic observability, which is defined as the ability of a signal controller to estimate traffic state from connected vehicle (CV), loop detector data, or both. This paper aims to quantify the effects of traffic observability on network-level performance, traffic progression, and travel time reliability, and to quantify those effects for vehicle classes and major and minor directions in an arterial corridor. Specifically, we incorporated loop detector and CV data into an adaptive signal controller and measured several mobility- and event-based performance metrics under different degrees of traffic observability (i.e., detector-only, CV-only, and CV and loop detector data) with various CV market penetration rates. A real-world arterial street of 10 intersections in Seattle, Washington was simulated in Vissim under peak hour traffic demand level with transit vehicles. The results showed that a 40% CV market share was required for the adaptive signal controller using only CV data to outperform signal control with only loop detector data. At the same market penetration rate, signal control with CV-only data resulted in the same traffic performance, progression quality, and travel time reliability as the signal control with CV and loop detector data. Therefore, the inclusion of loop detector data did not further improve traffic operations when the CV market share reached 40%. Integrating 10% of CV data with loop detector data in the adaptive signal control improved traffic performance and travel time reliability.

Towards a Multi Objective Path Optimisation for Car Navigation

2011 ◽  
Vol 65 (1) ◽  
pp. 125-144 ◽  
Author(s):  
Ching-Sheng Chiu ◽  
Chris Rizos
Keyword(s):  
Selection Criteria ◽  
Empirical Studies ◽  
Route Selection ◽  
Multi Objective ◽  
Optimal Paths ◽  
Proposed Model ◽  
Shortest Distance ◽  
Shortest Path Algorithms ◽  
Car Navigation System ◽  
Car Navigation

In a car navigation system the conventional information used to guide drivers in selecting their driving routes typically considers only one criterion, usually the Shortest Distance Path (SDP). However, drivers may apply multiple criteria to decide their driving routes. In this paper, possible route selection criteria together with a Multi Objective Path Optimisation (MOPO) model and algorithms for solving the MOPO problem are proposed. Three types of decision criteria were used to present the characteristics of the proposed model. They relate to the cumulative SDP, passed intersections (Least Node Path – LNP) and number of turns (Minimum Turn Path – MTP). A two-step technique which incorporates shortest path algorithms for solving the MOPO problem was tested. To demonstrate the advantage that the MOPO model provides drivers to assist in route selection, several empirical studies were conducted using two real road networks with different roadway types. With the aid of a Geographic Information System (GIS), drivers can easily and quickly obtain the optimal paths of the MOPO problem, despite the fact that these paths are highly complex and difficult to solve manually.

scholarly journals Stop Plan of Express and Local Train for Regional Rail Transit Line

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Qin Luo ◽  
Yufei Hou ◽  
Wei Li ◽  
Xiongfei Zhang
Keyword(s):  
Energy Consumption ◽  
Travel Time ◽  
Programming Model ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Long Distance ◽  
Passenger Travel ◽  
Urban Rail ◽  
Proposed Model ◽  
Rail Line

The urban rail transit line operating in the express and local train mode can solve the problem of disequilibrium passenger flow and space and meet the rapid arrival demand of long-distance passengers. In this paper, the Logit model is used to analyze the behavior of passengers choosing trains by considering the sensitivity of travel time and travel distance. Then, based on the composition of passenger travel time, an integer programming model for train stop scheme, aimed at minimizing the total passenger travel time, is proposed. Finally, combined with a certain regional rail line in Shenzhen, the plan is solved by genetic algorithm and evaluated through the time benefit, carrying capacity, and energy consumption efficiency. The simulation result shows that although the capacity is reduced by 6 trains, the optimized travel time per person is 10.34 min, and the energy consumption is saved by about 16%, which proves that the proposed model is efficient and feasible.

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