Setting Headways on a Bus Route under Uncertain Conditions

Chen, Weiya; Liu, Xin; Chen, Dingfang; Pan, Xuewei

doi:10.3390/su11102823

Cited by 8 publications

(4 citation statements)

References 25 publications

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“…The travel time of the bus k between the two adjacent stops i and i + 1 includes the running time, delay on the road, and delay at the intersections [13], [30]. There are many reasons for the delay, such as distance, change of lane number, signal control, and interference from pedestrians and other vehicles.…”

Section: Methodsmentioning

confidence: 99%

Estimating Travel Times of Mid-Volume Buses Considering Right-of-Way Variation

et al. 2020

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Transit agencies often provide estimates of bus travel times to downstream stops. This study aims to improve the perceived reliability of bus transit systems and enhance their competitiveness. This study considers the characteristics of low headway and high demand for mid-volume bus lanes. Considering the variation in right-of-way with respect to both time and space, a stop-based bus route is built to divide the road into sections. Available real-time data from a schedule-based mid-volume bus route are used, including bus global positioning system (GPS) data, road condition information, and weather. Based on the accelerated failure time (AFT) model, a dynamic travel time model considering right-of-way variation is established to estimate bus travel times between adjacent stops and explore the specific impact of bus right-of-way variation on the travel time. The AFT model is chosen because it can reveal the significance of different variables to estimate travel times, and simultaneously estimate expected travel times as well as travel time uncertainty. The experimental results indicate that bus right-of-way variation significantly affects travel times. In contrast to the linear model, the parameter estimated by the AFT model conforms better to expectations, especially for long-distance travel. INDEX TERMSTravel time, mid-volume bus, accelerated failure time model, GPS data, right-of-way variation. YI LI received the B.S. degree in transportation engineering from Southeast University, Nanjing, China, in 2013, where she is currently pursuing the M.S. degree in transportation planning and management. From 2017 to 2019, as the Project Leader or a Key Researcher, she has been involved in a number of transportation planning projects in China. Her research interests include transport modeling and transportation planning. Ms. Li, as a Key Researcher, she and her team had obtained the Second Prize in the National Competition of Transport Science and Technology for students in China, from 2014 to 2015.JUMEI ZHU is currently pursuing the bachelor's degree in traffic engineering with Southeast University, Nanjing, China. From 2018 to 2019, as a Key Researcher, her team's SRTP project was rated as a provincial university student innovation and entrepreneurship project.

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Section: Methodsmentioning

confidence: 99%

Estimating Travel Times of Mid-Volume Buses Considering Right-of-Way Variation

et al. 2020

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“…The objective was to maximize passenger volume. Chen et al (2019) established a bus headway allocation model, where the travel time was assumed to be normally distributed random variable. The objective was to maximize the passenger volume and minimize the number of passengers failed to board.…”

Section: Bus Timetable Optimizationmentioning

confidence: 99%

“…The objective was to maximize passenger volume. Chen et al. (2019) established a bus headway allocation model, where the travel time was assumed to be normally distributed random variable.…”

Section: Literature Reviewmentioning

confidence: 99%

Data-driven bus timetabling with spatial-temporal travel time

Ming

et al. 2022

IMDS

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PurposeTravel time at inter-stops is a set of important parameters in bus timetabling, which is usually assumed to be normal (log-normal) random variable in literature. With the development of digital technology and big data analytics ability in the bus industry, practitioners prefer to generate deterministic travel time based on the on-board GPS data under maximum probability rule and mean value rule, which simplifies the optimization procedure, but performs poorly in the timetabling practice due to the loss of uncertain nature on travel time. The purpose of this study is to propose a GPS-data-driven bus timetabling approach with consideration of the spatial-temporal characteristic of travel time.Design/methodology/approachThe authors illustrate that the real-life on-board GPS data does not support the hypothesis of normal (log-normal) distribution on travel time at inter-stops, thereby formulating the travel time as a scenario-based spatial-temporal matrix, where K-means clustering approach is utilized to identify the scenarios of spatial-temporal travel time from daily observation data. A scenario-based robust timetabling model is finally proposed to maximize the expected profit of the bus carrier. The authors introduce a set of binary variables to transform the robust model into an integer linear programming model, and speed up the solving process by solution space compression, such that the optimal timetable can be well solved by CPLEX.FindingsCase studies based on the Beijing bus line 628 are given to demonstrate the efficiency of the proposed methodology. The results illustrate that: (1) the scenario-based robust model could increase the expected profits by 15.8% compared with the maximum probability model; (2) the scenario-based robust model could increase the expected profit by 30.74% compared with the mean value model; (3) the solution space compression approach could effectively shorten the computing time by 97%.Originality/valueThis study proposes a scenario-based robust bus timetabling approach driven by GPS data, which significantly improves the practicality and optimality of timetable, and proves the importance of big data analytics in improving public transport operations management.

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“…An enumeration combining a recursive algorithm under Monte Carlo random simulation conditions was designed to solve the problem. The results showed that the model could reduce the wait time of passengers and attract more passengers to travel by bus [30]. Bányai describes a real-time scheduling optimization model focusing on the energy efficiency of the operation and introduces a mathematical model of last mile delivery problems including scheduling and assignment problems.…”

Section: Introductionmentioning

confidence: 99%

Combinational Scheduling Model Considering Multiple Vehicle Sizes

Gong

2019

Sustainability

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Urban public transport is an effective way to solve urban traffic problems and promote sustainable development of urban traffic. A scientific operation scheduling system has an important guiding significance for optimizing the configuration of urban public transport capacity resources, improving the level of operation organization and management, and providing for the sustainability of the transportation system. According to the inhomogeneous distribution of passenger flow along transit lines, this study develops a combinational scheduling model in which the enterprise supplies zonal service based on regular service. The objective function minimizes the sum of passenger travel cost and operation cost, and the simulated annealing algorithm is designed to solve the optimization model. This paper abstracts an ideal example by taking a real-world case of Bus Line 131 in Lanzhou, China. The numerical example is used to verify the validity of the model and algorithm. Results show that the combinational operation scheme can effectively satisfy passengers’ demand and reduce the total cost by 7.03% in comparison with the regular operation system. The optimal combinational system with the lowest total cost can increase the vehicle load factor and improve the utilization ratio.

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Setting Headways on a Bus Route under Uncertain Conditions

Cited by 8 publications

References 25 publications

Estimating Travel Times of Mid-Volume Buses Considering Right-of-Way Variation

Estimating Travel Times of Mid-Volume Buses Considering Right-of-Way Variation

Data-driven bus timetabling with spatial-temporal travel time

Combinational Scheduling Model Considering Multiple Vehicle Sizes

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