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 Reducing Bus Bunching with Asynchronous Multi-Agent Reinforcement Learning

2021 ◽  
Author(s):  
Jiawei Wang ◽  
Lijun Sun
Keyword(s):  
Reinforcement Learning ◽  
Traffic Control ◽  
Inductive Learning ◽  
Traffic Conditions ◽  
Passenger Demand ◽  
Smart Card Data ◽  
Proposed Model ◽  
Bus Bunching ◽  
Multi Agent ◽  
Bus Services

The bus system is a critical component of sustainable urban transportation. However, due to the significant uncertainties in passenger demand and traffic conditions, bus operation is unstable in nature and bus bunching has become a common phenomenon that undermines the reliability and efficiency of bus services. Despite recent advances in multi-agent reinforcement learning (MARL) on traffic control, little research has focused on bus fleet control due to the tricky asynchronous characteristic---control actions only happen when a bus arrives at a bus stop and thus agents do not act simultaneously. In this study, we formulate route-level bus fleet control as an asynchronous multi-agent reinforcement learning (ASMR) problem and extend the classical actor-critic architecture to handle the asynchronous issue. Specifically, we design a novel critic network to effectively approximate the marginal contribution for other agents, in which graph attention neural network is used to conduct inductive learning for policy evaluation. The critic structure also helps the ego agent optimize its policy more efficiently. We evaluate the proposed framework on real-world bus services and actual passenger demand derived from smart card data. Our results show that the proposed model outperforms both traditional headway-based control methods and existing MARL methods.

scholarly journals Multiperspective Bus Route Planning in a Stackelberg Game Framework

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Xinyu Liu ◽  
Jie Yu ◽  
Xiaoguang Yang ◽  
Weijie Tan
Keyword(s):  
Service Providers ◽  
Stackelberg Game ◽  
Route Planning ◽  
Government Agencies ◽  
Fleet Size ◽  
Bisection Algorithm ◽  
Proposed Model ◽  
Bus Route Planning ◽  
The Impact

Bus route planning is a challenging task due to multiple perspective interactions among passengers, service providers, and government agencies. This paper presents a multidimensional Stackelberg-game-based framework and mathematical model to best trade off the decisions of multiple stakeholders that previous literature rarely captures, i.e., governments, service providers, and passengers, in planning a new bus route or adjusting an existing one. The proposed model features a bilevel structure with the upper level reflecting the perspective of government agencies in subsidy allocation and the lower level representing the decisions of service providers in dispatching frequency and bus fleet size design. The bilevel model is framed as a Stackelberg game where government agencies take the role of “leader” and service providers take the role of “follower” with social costs and profits set as payoffs, respectively. This Stackelberg-game-based framework can reflect the decision sequence of both participants as well as their competition or collaboration relationship in planning a bus route. The impact of such decisions on the mode and route choices of passengers is captured by a Nested Logit model. A partition-based bisection algorithm is developed to solve the proposed model. Results from a case study in Shanghai validate the effectiveness and performance of the proposed model and algorithm.

scholarly journals The Displacement of Santiago de Chile’s Downtown during 1990–2015: Travel Time Effects on Eradicated Population

2019 ◽  
Vol 12 (1) ◽  
pp. 289 ◽  
Author(s):  
Gonzalo Suazo-Vecino ◽  
Juan Carlos Muñoz ◽  
Luis Fuentes Arce
Keyword(s):  
Travel Time ◽  
Surface Area ◽  
Low Income ◽  
Central Area ◽  
Time Effects ◽  
Socioeconomic Groups ◽  
Santiago De Chile ◽  
Average Travel Time ◽  
The Impact ◽  
The City

The center of activities of Santiago de Chile has been continuously evolving towards the eastern part of the city, where the most affluent residents live. This paper characterizes the direction and magnitude of this evolution through an indicator stating how much the built surface area for service purposes grows in different areas in the city. To identify the impact of this evolution, we compare residents’ travel-time distributions from different sectors in the city to the central area. This travel-time comparison is focused on the sectors where informal settlements were massively eradicated between 1978–1985 and those areas where the settlements were relocated. This analysis show that this policy and the consequent evolution of the city were detrimental to the affected families, significantly increasing average travel time to the extended center of the city and inequality among different socioeconomic groups in the city. Although the phenomenon is quite visible to everyone, it has not received any policy reaction from the authority. These findings suggest that middle and low-income sectors would benefit if policies driving the evolution of the center of activities towards them were implemented.

scholarly journals The Influence of Introducing Autonomous Vehicles on Conventional Transport Modes and Travel Time

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4163
Author(s):  
Jamil Hamadneh ◽  
Domokos Esztergár-Kiss
Keyword(s):  
Travel Time ◽  
Public Transport ◽  
Travel Behavior ◽  
Autonomous Vehicles ◽  
Fleet Size ◽  
The Public ◽  
Levels Of Automation ◽  
Multi Agent ◽  
Transport Modes ◽  
The Impact

Introducing autonomous vehicles (AVs) on the market is likely to bring changes in the mobility of travelers. In this work, extensive research is conducted to study the impact of different levels of automation on the mobility of people, and full driving automation needs further study because it is still under development. The impacts of AVs on travel behavior can be studied by integrating AVs into activity-based models. The contribution of this study is the estimation of AVs’ impacts on travelers’ mobility when different travel demands are provided, and also the estimation of AVs’ impact on the modal share considering the different willingness of pay to travel by AVs. This study analyses the potential impacts of AVs on travel behavior by investigating a sample of 8500 travelers who recorded their daily activity plans in Budapest, Hungary. Three scenarios are derived to study travel behavior and to find the impacts of the AVs on the conventional transport modes. The scenarios include (1) a simulation of the existing condition, (2) a simulation of AVs as a full replacement for conventional transport modes, and (3) a simulation of the AVs with conventional transport modes concerning different marginal utilities of travel time in AVs. The simulations are done by using the Multi-Agent Transport Simulation (MATSim) open-source software, which applies a co-evolutionary optimization algorithm. Using the scenarios in the study, we develop a base model, determine the required fleet size of AVs needed to fulfill the demand of the different groups of travelers, and predict the new modal shares of the transport modes when AVs appear on the market. The results demonstrate that the travelers are exposed to a reduction in travel time once conventional transport modes are replaced by AVs. The impact of the value of travel time (VOT) on the usage of AVs and the modal share is demonstrated. The decrease in the VOT of AVs increases the usage of AVs, and it particularly decreases the usage of cars even more than other transport modes. AVs strongly affect the public transport when the VOT of AVs gets close to the VOT of public transport. Finally, the result shows that 1 AV can replace 7.85 conventional vehicles with acceptable waiting time.

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 Origin-Destination-Based Travel Time Reliability under Different Rainfall Intensities: An Investigation Using Open-Source Data

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Qi Zhang ◽  
Hong Chen ◽  
Hongchao Liu ◽  
Wei Li ◽  
Yibin Zhang
Keyword(s):  
Open Source ◽  
Travel Time ◽  
Ad Hoc ◽  
Rainfall Intensity ◽  
Weather Data ◽  
Travel Time Reliability ◽  
Time Data ◽  
Rainy Weather ◽  
Average Travel Time ◽  
The Impact

Origin-destination- (O-D-) based travel time reliability (TTR) is fundamental to next-generation navigation tools aiming to provide both travel time and reliability information. While previous works are mostly focused on route-based TTR and use either ad hoc data or simulation in the analyses, this study uses open-source Uber Movement and Weather Underground data to systematically analyze the impact of rainfall intensity on O-D-based travel time reliability. The authors classified three years of travel time data in downtown Boston into one hundred origin-destination pairs and integrated them with the weather data (rain). A lognormal mixture model was applied to fit travel time distributions and calculate the buffer index. The median, trimmed mean, interquartile range, and one-way analysis of variance were used for quantification of the characteristics. The study found some results that tended to agree with the previous findings in the literature, such that, in general, rain reduces the O-D-based travel time reliability, and some seemed to be unique and worthy of discussion: firstly, although in general the reduction in travel time reliability gets larger as the intensity of rainfall increases, it appears that the change is more significant when rainfall intensity changes from light to moderate but becomes fairly marginal when it changes from normal to light or from moderate to extremely intensive; secondly, regardless of normal or rainy weather, the O-D-based travel time reliability and its consistency in different O-D pairs with similar average travel time always tend to improve along with the increase of average travel time. In addition to the technical findings, this study also contributes to the state of the art by promoting the application of real-world and publicly available data in TTR analyses.

scholarly journals An SVEIRE Model of Tuberculosis to Assess the Effect of an Imperfect Vaccine and Other Exogenous Factors

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 327 ◽  
Author(s):  
Fatima Sulayman ◽  
Farah Aini Abdullah ◽  
Mohd Hafiz Mohd
Keyword(s):  
Numerical Simulation ◽  
Mathematical Model ◽  
Disease Burden ◽  
Backward Bifurcation ◽  
Effective Control ◽  
General Effect ◽  
Tuberculosis Transmission ◽  
Proposed Model ◽  
Exogenous Factors ◽  
The Impact

This study extends a deterministic mathematical model for the dynamics of tuberculosis transmission to examine the impact of an imperfect vaccine and other exogenous factors, such as re-infection among treated individuals and exogenous re-infection. The qualitative behaviors of the model are investigated, covering many distinct aspects of the transmission of the disease. The proposed model is observed to show a backward bifurcation, even when Rv<1. As such, we assume that diminishing Rv to less than unity is not effective for the elimination of tuberculosis. Furthermore, the results reveal that an imperfect tuberculosis vaccine is always effective at reducing the spread of infectious diseases within the population, though the general effect increases with the increase in effectiveness and coverage. In particular, it is shown that a limited portion of people being vaccinated at steady-state and vaccine efficacy assume a equivalent role in decreasing disease burden. From the numerical simulation, it is shown that using an imperfect vaccine lead to effective control of tuberculosis in a population, provided that the efficacy of the vaccine and its coverage are reasonably high.

The impact of local processes and the prohibition of multiple links in the topological properties of directed complex networks

2015 ◽  
Vol 26 (12) ◽  
pp. 1550142 ◽  
Author(s):  
J. Esquivel-Gómez ◽  
P. D. Arjona-Villicaña ◽  
J. Acosta-Elías
Keyword(s):  
Numerical Simulation ◽  
Complex Networks ◽  
Real World ◽  
Dynamic Properties ◽  
Topological Properties ◽  
A Value ◽  
Proposed Model ◽  
Accelerated Growth ◽  
Dynamical Properties ◽  
The Impact

Local processes exert influence on the growth and evolution of complex networks, which in turn shape the topological and dynamic properties of these networks. Some local processes have been researched, for example: Addition of nodes and links, rewiring of links between nodes, accelerated growth, link removal, aging, copying and multiple links prohibition. These processes impact directly into the topological and dynamical properties of complex networks. This paper introduces a new model for growth of directed complex networks which incorporates the prohibition of multiple links, addition of nodes and links, and rewiring of links. This paper also reports on the impact that these processes have in the topological properties of the networks generated with the proposed model. Numerical simulation shows that, when the frequency of rewiring increases in the proposed model, the γ exponent of the in-degree distribution approaches a value of 1.1. When the frequency of adding new links increases, the γ exponent approaches 1. That is the proposed model is able to generate all exponent values documented in real-world networks which range 1.05 < γ < 8.94.

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 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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