Optimizing Bus Line Based on Metro-Bus Integration

Wei, Junjun; Long, Kejun; Gu, Jian; Ju, Qingling; Zhu, Piao

doi:10.3390/su12041493

Cited by 13 publications

(12 citation statements)

References 28 publications

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“…Moreover, Adi et al [86] reviewed the process of machine learning analysis in IoT data generation, and developed a framework to make suitable applications to learn from other IoT applications. Finally, Wei et al [87] considered bus line optimization based on the metro-bus relationship and competition. They also developed a quantitative methodology to evaluate their approach.…”

Section: Iot Analytics In Itsmentioning

confidence: 99%

Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems

et al. 2021

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With the emergence of fog and edge computing, new possibilities arise regarding the data-driven management of citizens’ mobility in smart cities. Internet of Things (IoT) analytics refers to the use of these technologies, data, and analytical models to describe the current status of the city traffic, to predict its evolution over the coming hours, and to make decisions that increase the efficiency of the transportation system. It involves many challenges such as how to deal and manage real and huge amounts of data, and improving security, privacy, scalability, reliability, and quality of services in the cloud and vehicular network. In this paper, we review the state of the art of IoT in intelligent transportation systems (ITS), identify challenges posed by cloud, fog, and edge computing in ITS, and develop a methodology based on agile optimization algorithms for solving a dynamic ride-sharing problem (DRSP) in the context of edge/fog computing.These algorithms allow us to process, in real time, the data gathered from IoT systems in order to optimize automatic decisions in the city transportation system, including: optimizing the vehicle routing, recommending customized transportation modes to the citizens, generating efficient ride-sharing and car-sharing strategies, create optimal charging station for electric vehicles and different services within urban and interurban areas. A numerical example considering a DRSP is provided, in which the potential of employing edge/fog computing, open data, and agile algorithms is illustrated.

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Section: Iot Analytics In Itsmentioning

confidence: 99%

Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems

et al. 2021

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“…The transfers and connections between URT and buses is a popular research topic in passenger multimodal transport. The two main research areas are a) optimization of the layout of station or transfer facilities [6]- [8] and b) optimization of the lines or schedules of the bus for connecting to URT [9]- [12]. In view of the unexpected interruption of URT, Jin et al [13] proposed optimizing the bus-bridge service to better evacuate stranded passengers.…”

Section: Related Workmentioning

confidence: 99%

“…According to these studies, adjustment means and timetable experience in the train operation plan provide some reference value for this work. Additionally, in almost all the aforementioned studies and URT-bus studies [9]- [12], the objective was to reduce passenger waiting (transfer) time. Therefore, in the present study, the minimum passenger waiting time at the transfer station was one of the optimization goals.…”

Section: Related Workmentioning

confidence: 99%

Optimization of Urban Rail Transit Connection Scheme for Evacuating Large Volumes of Arriving Railway Passengers

Feng

Song

et al. 2020

IEEE Access

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Urban rail transit (URT) is an essential mode of travel used for evacuating passengers from railway stations. To provide timely and safe connecting services for passengers arriving at railway hubs, it is recommended for URT operators to develop a coordinated connection scheme considering the periodic incoming volume of railway passengers. According to the fluctuation characteristics of the large passenger flows in railways, this paper presents a reasonable scheme for increasing the capacity of URT trains. First, a method for calculating the effective evacuation capacity of URT trains and determining the time range that should be optimized according to the transportation capacity matching degree is proposed. The dwell time and departure interval are taken as decision variables in this adjustment period. Two constraints are considered: capacity convergence and the maximum safe capacity of the platform. Based on the consideration of reserved capacity for subsequent sections, the objective is to optimize the degree of matching between the effective evacuation capacity of the URT and the transfer demand of the arriving railway passengers. A multi-objective nonlinear integer-programming model is established for the coordinated connection of URT trains with large passenger flows, and a solution process of a train connection scheme is designed that involves a genetic algorithm. Finally, the effectiveness of the proposed model and algorithm is analyzed and verified by considering the Shanghai Hongqiao Hub-a transfer station between a high-speed railway and URT-as an example. INDEX TERMS Urban rail transit, connection scheme, passenger evacuation, integer programming, genetic algorithm.

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“…With the advancement of urbanization, rail transit is now being rapidly constructed in many large cities so as to relieve the pressure of passenger flow. Rail transit routes are usually constructed along the passenger corridors to ensure attracting sufficient passengers when they are in operation [7]. Nevertheless, the daily demand of these passenger corridors still needs to be served by bus lines before rail transit routes are constructed.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Bus Rerouting to Alleviate the Impact of Rail Transit Construction

Wang

Chen

et al. 2022

Journal of Advanced Transportation

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In urban areas, rail transit construction affects the travel time and the efficiency of bus lines along the surrounding roads. To ensure service qualities, this study proposes a two-stage optimization method for bus rerouting to alleviate the impact of rail construction. In the first stage, the effect of the construction on bus operation is evaluated based upon one indicator, namely the increased rate of bus turnaround time. The affected lines that need rerouting are sifted by the indicator. For each rerouting line, we design two adjustment patterns and associated candidate alternative rerouting sets. In the second stage, the bus network alteration optimization model is developed to maximize the number of passengers served. The model considers several practical constraints, including service connectivity, rationality of bus travel time, and budget for building new bus stops. It realizes a systematic optimization of bus rerouting lines affected by rail transit construction. The proposed method is applied to Ning-Ma intercity railway in Ma’anshan, China to analyze the impact of construction on the surrounding bus lines and obtain the optimal adjustment scheme. The results show that the proposed method is capable of effectively reducing the negative effects caused by rail transit construction and meantime guaranteeing bus service quality.

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Optimizing Bus Line Based on Metro-Bus Integration

Cited by 13 publications

References 28 publications

Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems

Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems

Optimization of Urban Rail Transit Connection Scheme for Evacuating Large Volumes of Arriving Railway Passengers

Optimization of Bus Rerouting to Alleviate the Impact of Rail Transit Construction

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