A Data-Driven and Optimal Bus Scheduling Model With Time-Dependent Traffic and Demand

Wang, Yuan; Zhang, Dongxiang; Hu, Lu; Yang, Yang; Lee, Loo Hay

doi:10.1109/tits.2016.2644725

Cited by 88 publications

(52 citation statements)

References 27 publications

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“…Based on bee colony optimization, Nikolic and Teodorovic [30] developed a swarm intelligence model for the transport network design problem (TNDP) by considering bus frequency and transport network optimization on each of the bus routes simultaneously. In spite of the high value for bus frequency optimization, Wang et al [31] leveraged millions of bus transaction records, which can generate passengers' boarding and alighting information, to infer the time-dependent traffic and customer demand.…”

Section: Transport Network Optimizationmentioning

confidence: 99%

Public Traffic Congestion Estimation Using an Artificial Neural Network

Wang

Dong

2020

IJGI

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Alleviating public traffic congestion is an efficient and effective way to improve the travel time reliability and quality of public transport services. The existing public network optimization models usually ignored the essential impact of public traffic congestion on the performance of public transport service. To address this problem, this study proposes a data-based methodology to estimate the traffic congestion of road segments between bus stops (RSBs). The proposed methodology involves two steps: (1) Extracting three traffic indicators of the RSBs from smart card data and bus trajectory data; (2) The self-organizing map (SOM) is used to cluster and effectively recognize traffic patterns embedded in the RSBs. Furthermore, a congestion index for ranking the SOM clusters is developed to determine the congested RSBs. A case study using real-world datasets from a public transport system validates the proposed methodology. Based on the congested RSBs, an exploratory example of public transport network optimization is discussed and evaluated using a genetic algorithm. The clustering results showed that the SOM could suitably reflect the traffic characteristics and estimate traffic congestion of the RSBs. The results obtained in this study are expected to demonstrate the usefulness of the proposed methodology in sustainable public transport improvements. these network optimization models developed in the above-mentioned articles had been usually applied in uncongested road segments.Public traffic condition refers to the traffic volume of the road network and its dynamic spatial-temporal distribution, which can reflect the degree of congestion [8]. Recently, the traffic congestion impacts of bus operations on a road segment or a corridor have been investigated by researchers [9,10]. These congestion effects mainly include the effects of bus stop design, bus travel time, and bus priority options such as exclusive bus lanes or priority signals for buses [9]. Understanding these congestion impacts can help operators to identify the effectiveness of transport network optimization in relieving congested areas or congested routes. For example, under congested traffic conditions, it is difficult for buses to return to the driving lane, which leads to a longer travel time after picking-up/dropping off passengers at stops [11]. Furthermore, the bus travel time variation dominated by traffic congestion often results in unreliable service, which has negative impacts on both the operators and passengers [12]. Previous studies have pointed out that well-located stops have the potential to alleviate the impact of traffic congestion [3]. Therefore, it is critical to optimize PTN by considering the impact of traffic congestion in order to achieve a high level of public transport service and improve travel time reliability.In this study, we proposed a data-based methodology to estimate the traffic congestion of road segments between bus stops (RSBs) using a self-organizing feature map (SOM). The SOM was used to cluster and effective...

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Section: Transport Network Optimizationmentioning

confidence: 99%

Public Traffic Congestion Estimation Using an Artificial Neural Network

Wang

Dong

2020

IJGI

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“…This section compares our system with related work on transit timetable scheduling. A number of studies have been conducted to provide timetabling strategies for various objectives: (1) minimizing average waiting time [27] (2) minimizing transfer time and cost [7][12] [24], (3) minimizing total travel time [17], (4) maximizing number of simultaneous bus arrivals [9], [13], (5) minimizing the cost of transit operation [26], (6) minimizing a mix of cost (both the user's and the operator's) [6].…”

Section: Related Work and Challengesmentioning

confidence: 99%

Data-Driven Optimization of Public Transit Schedule

et al. 2019

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Bus transit systems are the backbone of public transportation in the United States. An important indicator of the quality of service in such infrastructures is on-time performance at stops, with published transit schedules playing an integral role governing the level of success of the service. However there are relatively few optimization architectures leveraging stochastic search that focus on optimizing bus timetables with the objective of maximizing probability of bus arrivals at timepoints with delays within desired on-time ranges. In addition to this, there is a lack of substantial research considering monthly and seasonal variations of delay patterns integrated with such optimization strategies. To address these, this paper makes the following contributions to the corpus of studies on transit on-time performance optimization: (a) an unsupervised clustering mechanism is presented which groups months with similar seasonal delay patterns, (b) the problem is formulated as a single-objective optimization task and a greedy algorithm, a genetic algorithm (GA) as well as a particle swarm optimization (PSO) algorithm are employed to solve it, (c) a detailed discussion on empirical results comparing the algorithms are provided and sensitivity analysis on hyper-parameters of the heuristics are presented along with execution times, which will help practitioners looking at similar problems. The analyses conducted are insightful in the local context of improving public transit scheduling in the Nashville metro region as well as informative from a global perspective as an elaborate case study which builds upon the growing corpus of empirical studies using nature-inspired approaches to transit schedule optimization.

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“…However, the analyses of these activities are very challenging due to the inherent 'Big' and/or 'Fragmentary' nature of the data. Since the last decade, many works have been reported in the literature on their processing and development of various support systems (SS) targeting different urban applications (UA), e.g., real-time transportation operation and management, urban planning, food and water stock planning, optimal resource allocation, and crowd safety management [24], [25], [26].…”

Section: Introductionmentioning

confidence: 99%

“…Gal-Tzur et al [42] discussed different issues and challenges of social network's impact on transport services and related policy development. The usage of social network for transportation data collection, crowd modeling, and crowd size estimation have been previously reported [43], [44], [45], [46], [47], [26]. Ma et al [48] introduced a distributed stream-based framework which fused various information to visualize data (at the different stacks) on a map.…”

Section: Introductionmentioning

confidence: 99%

Advances in Crowd Analysis for Urban Applications Through Urban Event Detection

Kaiser

Lwin

Mahmud

et al. 2018

IEEE Trans. Intell. Transport. Syst.

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Abstract-The recent expansion of pervasive computing technology has contributed with novel means to pursue human activities in urban space. The urban dynamics unveiled by these means generate an enormous amount of data. These data are mainly endowed by portable and radio-frequency devices, transportation systems, video surveillance, satellites, unmanned aerial vehicles, and social networking services. This has opened a new avenue of opportunities, to understand and predict urban dynamics in detail, and plan various real-time services and applications in response to that. Over the last decade, certain aspects of the crowd, e.g. mobility, sentimental, size estimation and behavioral, have been analyzed in detail and the outcomes have been reported. This article mainly conducted an extensive survey on various data sources used for different urban applications, the state-of-the-art on urban data generation techniques and associated processing methods in order to demonstrate their merits and capabilities. Then, a possible crowd event detection framework is discussed which fuses data from all the available pervasive technology sources. In addition, available open-access crowd datasets for urban event detection are provided along with relevant Application Programming Interfaces, and finally, some open challenges and promising research directions are outlined.Index Terms-Urban sensing, pervasive technology, crowd mobility and management, information fusion, decision support system, benchmark datasets.

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A Data-Driven and Optimal Bus Scheduling Model With Time-Dependent Traffic and Demand

Cited by 88 publications

References 27 publications

Public Traffic Congestion Estimation Using an Artificial Neural Network

Public Traffic Congestion Estimation Using an Artificial Neural Network

Data-Driven Optimization of Public Transit Schedule

Advances in Crowd Analysis for Urban Applications Through Urban Event Detection

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