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.

An Optimization Model for Dynamic Speed Control in Urban Freeway Networks

2014 ◽  
Vol 668-669 ◽  
pp. 1458-1461
Author(s):  
Zhao Hong Zhang ◽  
Da Zhi Sun ◽  
Jin Peng Lv ◽  
Joseph Sai ◽  
M. Faruqi
Keyword(s):  
Optimization Model ◽  
Control Strategies ◽  
Optimal Solution ◽  
Speed Control ◽  
Speed Limits ◽  
Microscopic Traffic Simulation ◽  
Proposed Model ◽  
Average Travel Time ◽  
Freeway Networks ◽  
Urban Freeway

This paper introduced an optimization model to address dynamic speed control strategies for achieving network-wide speed harmonization. Genetic Algorithm (GA) was applied to search the optimal solution of the proposed model. During the search process, a computational fluid dynamics (CFD) based analytical model and microscopic traffic simulation VISSIM were applied to evaluate the performance of possible solutions. The proposed model can be used to determine the deployment of dynamic speed limits, the displayed speed limit, and the timing to change these speed limits. The proposed model was tested using VISSIM in an urban freeway network of about 12 miles long. Different simulation scenarios with varying AADT from 60,000 to 12,000 were tested. It was found that when properly implemented, dynamic speed control can improve traffic flow conditions, reduce congestion and emission, and enhance network throughput. For example, in the selected urban freeway network with the AADT of 80,000, the proposed dynamic speed control strategy can save 5% average travel time, reduce 9% of the vehicles with high collision risk and about 11% emission.

scholarly journals Improving Bus Operations through Integrated Dynamic Holding Control and Schedule Optimization

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Shuozhi Liu ◽  
Xia Luo ◽  
Peter J. Jin
Keyword(s):  
Numerical Simulation ◽  
Travel Time ◽  
Optimal Choice ◽  
Fleet Size ◽  
Slack Time ◽  
Proposed Model ◽  
Average Travel Time ◽  
Bus Bunching ◽  
Bus Services ◽  
The Impact

Bus bunching can lead to unreliable bus services if not controlled properly. Passengers will suffer from the uncertainty of travel time and the excessive waiting time. Existing dynamic holding strategies to address bus bunching have two major limitations. First, existing models often rely on large slack time to ensure the validity of the underlying model. Such large slack time can significantly reduce the bus operation efficiency by increasing the overall route travel times. Second, the existing holding strategies rarely consider the impact on the schedule planning. Undesirable results such as bus overloading issues arise when the bus fleet size is limited. This paper explores analytically the relationship between the slack time and the effect of holding control. The optimal slack time determined based on the derived relationship is found to be ten times smaller than in previous models based on numerical simulation results. An optimization model is developed with passenger-orient objective function in terms of travel cost and constraints such as fleet size limit, layover time at terminals, and other schedule planning factors. The optimal choice of control stops, control parameters, and slack time can be achieved by solving the optimization. The proposed model is validated with a case study established based on field data collected from Chengdu, China. The numerical simulation uses the field passenger demand, bus average travel time, travel time variance of road segments, and signal timings. Results show that the proposed model significantly reduce passengers average travel time compared with existing methods.

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.

The Optimization Model for Vehicle Routing Program Based on Travel Time Reliability

2014 ◽  
Vol 543-547 ◽  
pp. 12-15
Author(s):  
Hong Wei Ma
Keyword(s):  
Objective Function ◽  
Travel Time ◽  
Vehicle Routing ◽  
Optimization Model ◽  
Level Of Service ◽  
Travel Time Reliability ◽  
Program Problem ◽  
Proposed Model ◽  
Distribution Process ◽  
Delivery Vehicles

This paper analyzes the necessity of setting the travel time reliability as the optimization objective in distribution process. It based on traditional vehicle routing program models puts forward the problem of vehicle routing program problem on the basis of travel time reliability and establishes the corresponding optimization model. It also verifies the practicality of the proposed model in this paper by taking a logistic enterprise as an example. Therefore the established model of delivery vehicles which took travel time reliability as objective function has better application value for improving punctuality of distribution and level of service.

scholarly journals Multiobjective design of sustainable public transportation systems

2021 ◽  
Author(s):  
D.G. Rossit ◽  
S. Nesmachnow ◽  
J. Toutouh
Keyword(s):  
Public Transportation ◽  
Transportation Network ◽  
Complex Problem ◽  
Transportation Systems ◽  
Modern Cities ◽  
Proposed Model ◽  
Conflicting Objectives ◽  
Total Travel Time ◽  
Public Transportation Systems ◽  
Multiobjective Design

The design of the bus network is a complex problem in modern cities, since different conflicting objectives have to be considered, from both the perspective of bus companies and the citizens. This article presents a multiobjective model for designing a sustainable public transportation network that simultaneously optimizes the covered travel demands by passengers, the total travel time, and the generated pollution. The proposed model is solved using exact weighted sum and a heuristic procedure based on the standard shortest path problem. Preliminary tests were performed in small real-world instances of Montevideo, Uruguay. Experiments allowed obtaining a set of compromising solutions that in turn allow exploring different trade-off among the optimization criteria. The proposed heuristic was competitive, being able to find a good compromising solution in short computing times.

scholarly journals Optimal Transportation System & Resilience: A Study of Sindhudurg District Cashew Industry

2021 ◽  
Vol 36 (2) ◽  
pp. 171-178
Author(s):  
Dr. Shilpa C. Shinde ◽  
N. Balasubramanian
Keyword(s):  
Optimization Model ◽  
Transportation Network ◽  
System Optimization ◽  
Transportation System ◽  
Environmental Benefits ◽  
Route Optimization ◽  
Value Chains ◽  
Production Networks ◽  
Network Restoration ◽  
Transportation Resilience

Value chains have increased in intricacy and length in recent decades as firms prepare to tackle expanding globalisation with increased peripheral advancements. This involves the adoption of leaner supply chains as well as the formation of ecosystems that provide a stable environment and a constant flow of operations. However, because disruptions are inevitable in today's world, the operational models must be tuned to handle any risks. Complex production networks are designed for a variety of reasons, including cost, proximity to markets, and mass standardisation, but not necessarily for transparency or resilience. Any organization's supply chain operations can be a cause of vulnerability or resilience, depending on its capacity to assess risks, adopt risk mitigation methods, and develop effective business continuity plans. Transportation is the most important component in value chains, and transportation resilience is critical in recovering production networks through precise scheduling and achieving resilience indicators such as lowest trip time, minimum cost, and route optimization, among others. The goal of this research is to clarify the key issues in network restoration scheduling and to offer a unique resilience-based optimization model for post-disaster transportation network restoration, in order to clear up theoretical and empirical ambiguity. Cashew industry which is seasonal as well as face many disruptions in production and processing stages was considered for the study. The study's objectives are (a) Study resilience indexes and its influence on transportation system optimization and (b) Study influence of resilience indexes on industry-based challenges with cashew product. The study objectives were addressed utilising an optimization model based on OR techniques and computer programming. The ideal solution for transportation cost, time, and efficiency can be obtained with the least amount of adjustment and analysis time, allowing cashew farmers to take advantage of transportation resilience and earn financial and environmental benefits.

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.

A Platooning Strategy for Automated Vehicles in the Presence of Speed Limit Fluctuations

2018 ◽  
Vol 2672 (20) ◽  
pp. 154-161 ◽  
Author(s):  
Sina Arefizadeh ◽  
Alireza Talebpour
Keyword(s):  
Velocity Profile ◽  
Travel Time ◽  
Transportation Network ◽  
Congestion Management ◽  
Transportation Systems ◽  
Driver Model ◽  
Speed Limit ◽  
Travel Time Reliability ◽  
Automated Vehicles ◽  
Car Following Model

Platooning is expected to enhance the efficiency of operating automated vehicles. The positive impacts of platooning on travel time reliability, congestion, emissions, and energy consumption have been shown for homogenous roadway segments. However, the transportation system consists of inhomogeneous segments, and understanding the full impacts of platooning requires investigation in a realistic setup. One of the main reasons for inhomogeneity is speed limit fluctuations. Speed limit changes frequently throughout the transportation network, due to safety-related considerations (e.g., changes in geometry and workzone operations) or congestion management schemes (e.g., speed harmonization systems). In the current transportation systems with human-driven vehicles, these speed drops can potentially result in shockwave formation, which can cause travel time unreliability. Automated vehicles, however, have the potential to prevent shockwave formation and propagation and, therefore, enhance travel time reliability. Accordingly, this study presents a constant time headway strategy for automated vehicle platooning to ensure accurate tracking of any velocity profile in the presence of speed limit fluctuations. The performance of the presented platooning strategy is compared with Gipps’ car-following model and intelligent driver model, as representatives of regular non-automated vehicles. Simulation results show that implementing a fully autonomous system prevents shockwave formation and propagation, and enhances travel time reliability by accurately tracking the desired velocity profile. Moreover, the performance of platoons of regular and automated vehicles is investigated in the presence of a speed drop. The results show that as the market penetration rate of automated vehicles increases, the platoon can track the velocity profile more accurately.

scholarly journals Impact of connected vehicle guidance information on network-wide average travel time

2017 ◽  
Vol 9 (1) ◽  
pp. 168781401668335 ◽  
Author(s):  
Jiangfeng Wang ◽  
Jiarun Lv ◽  
Qian Zhang ◽  
Xuedong Yan
Keyword(s):  
Travel Time ◽  
Data Exchange ◽  
Positive Impact ◽  
Penetration Rate ◽  
Transportation Systems ◽  
Time Variability ◽  
Connected Vehicle ◽  
Vehicle Guidance ◽  
Average Travel Time ◽  
The Impact

With the emergence of connected vehicle technologies, the potential positive impact of connected vehicle guidance on mobility has become a research hotspot by data exchange among vehicles, infrastructure, and mobile devices. This study is focused on micro-modeling and quantitatively evaluating the impact of connected vehicle guidance on network-wide travel time by introducing various affecting factors. To evaluate the benefits of connected vehicle guidance, a simulation architecture based on one engine is proposed representing the connected vehicle–enabled virtual world, and connected vehicle route guidance scenario is established through the development of communication agent and intelligent transportation systems agents using connected vehicle application programming interface considering the communication properties, such as path loss and transmission power. The impact of connected vehicle guidance on network-wide travel time is analyzed by comparing with non-connected vehicle guidance in response to different market penetration rate, following rate, and congestion level. The simulation results explore that average network-wide travel time in connected vehicle guidance shows a significant reduction versus that in non–connected vehicle guidance. Average network-wide travel time in connected vehicle guidance have an increase of 42.23% comparing to that in non-connected vehicle guidance, and average travel time variability (represented by the coefficient of variance) increases as the travel time increases. Other vital findings include that higher penetration rate and following rate generate bigger savings of average network-wide travel time. The savings of average network-wide travel time increase from 17% to 38% according to different congestion levels, and savings of average travel time in more serious congestion have a more obvious improvement for the same penetration rate or following rate.

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