Modeling level-of-safety for bus stops in China

Ye, Zhirui; Wang, Chao; Yu, Yongbo; Shi, Xiaomeng

doi:10.1080/15389588.2015.1133905

Cited by 26 publications

(13 citation statements)

References 20 publications

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“…Compared with on-line bus stops, buses at off-line stops need to search for an accessible gap from consecutive vehicles, which can cause additional time for off-line stop buses. With regartd to the location of the cross-section, two categories of bus stops can be identified: median and curbside ( 39 ). According to the above classifications, seven types of bus stop are most commonly observed in China.…”

Section: Case Studymentioning

confidence: 99%

Modeling Bus Capacity for Bus Stops Using Queuing Theory and Diffusion Approximation

Wang

Chen

et al. 2021

Transportation Research Record

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For bus service quality and line capacity, one critical influencing factor is bus stop capacity. This paper proposes a bus capacity estimation method incorporating diffusion approximation and queuing theory for individual bus stops. A concurrent queuing system between public transportation vehicles and passengers can be used to describe the scenario of a bus stop. For most of the queuing systems, the explicit distributions of basic characteristics (e.g., waiting time, queue length, and busy period) are difficult to obtain. Therefore, the diffusion approximation method was introduced to deal with this theoretical gap in this study. In this method, a continuous diffusion process was applied to estimate the discrete queuing process. The proposed model was validated using relevant data from seven bus stops. As a comparison, two common methods— Highway Capacity Manual (HCM) formula and M/M/S queuing model (i.e., Poisson arrivals, exponential distribution for bus service time, and S number of berths)—were used to estimate the capacity of the bus stop. The mean absolute percentage error (MAPE) of the diffusion approximation method is 7.12%, while the MAPEs of the HCM method and M/M/S queuing model are 16.53% and 10.23%, respectively. Therefore, the proposed model is more accurate and reliable than the others. In addition, the influences of traffic intensity, bus arrival rate, coefficient of variation of bus arrival headway, service time, coefficient of variation of service time, and the number of bus berths on the capacity of bus stops are explored by sensitivity analyses.

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Section: Case Studymentioning

confidence: 99%

Modeling Bus Capacity for Bus Stops Using Queuing Theory and Diffusion Approximation

Wang

Chen

et al. 2021

Transportation Research Record

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“…Several studies used regression analysis to quantify the frequency of bus crashes under different factors. Ye et al used principal component analysis and k-means clustering methods to model and quantify safety levels for bus stops [13]. Chimba et al assessed the impact of vehicle size on the frequency of accidents [14].…”

Section: Introductionmentioning

confidence: 99%

Risk Area Identification Model of Bus Bay Stops Based on Distribution of Conflicts

Ruan

Zhi

et al. 2021

Discrete Dynamics in Nature and Society

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Effective identification of the risk area of the bus bay stop is a prerequisite for the enhancement of traffic safety. This study proposes a method of identifying the risk area based on the distribution of traffic conflicts. Firstly, the traffic flow data of the bus stop is collected by drones and video recognition software, and the traffic flow characteristics of the bus stop are analyzed by the mathematical and statistical methods. Secondly, using the gray clustering evaluation theory, on the basis of the rasterization of the functional area of the bus bay stop, a risk level model based on the index system of conflict rate, conflict severity, and potential conflict risk is proposed. Finally, take a bus stop in Guangzhou as an example to verify the solution. The results show that the constructed model can effectively identify the risk areas of bus bay stops. The risk areas of the bus bay stops are concentrated in the middle and lower reaches of the bus stop, which proves that the impact of bus exiting the stop on the surrounding traffic is greater than the process of bus entering the stop; the traffic risk areas of lanes near the bus stop are concentrated, and the severity of conflicts is low. The traffic risk zone of the lane far away from the bus stop is widely distributed, and the severity of conflict is higher. The research results can provide a basis for the micro safety performance evaluation and safety optimization of bus bay stops, which has strong theoretical and practical significance.

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“…In addition, the literature is quite rich on risk assessment methods in several fields such as chemical, economic and financial, engineering, industrial, medical (e.g., Fine, 1971;CCPS, 1995;Andrews andMoss (2002) ISO 39001, 2009;Mullai, 2006). However, as far as the authors know, the literature is quite poor on bus crash risk assessment methods, since only a handful of recent studies have attempted to define some crash risk indexes (Tiboni and Rossetti, 2013;Mitsakis, 2015;Ye et al, 2016;Law et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Assessing the Risk of Bus Crashes in Transit Systems

Porcu¹

2021

European Transport/Trasporti Europei

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Although public transport buses may be considered a safe transportation mode, bus safety is a crucial issue from the perspectives of operators, passengers and local authorities owing to the relevant implications it generates. Therefore, assessing the risk of crashes on bus routes may help improve the safety performance of transit operators. Much research has identified patterns of bus crashes to understand the effects of many factors on the frequency and the severity of them. Conversely, to the best of our knowledge, the research measuring the risk of crashes in bus transit networks is seldom faced. This paper adjusts existing methods to assess the safety on bus transit networks by the integration of safety factors, prediction models and risk methods. More precisely, first, the methodology identifies several safety factors as well as the exposure risk factors. Second, this methodology specifies the risk components in terms of frequency, severity and exposure factors that may affect bus crashes and models their relationships in a risk function. Third, this methodology computes the risk of crashes for each route and provides a ranking of safety performance. A real case study demonstrates the feasibility of this methodology using 3,457 bus crashes provided by a mid-sized Italian bus operator. This experiment shows that transit managers could adopt this methodology to perform an accurate safety analysis on each route. Moreover, this methodology could be implemented in a road traffic safety management system in order to evaluate the risk of crashes on routes, monitor the safety performance of each route and qualify each route according to recent safety norms.

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Modeling level-of-safety for bus stops in China

Cited by 26 publications

References 20 publications

Modeling Bus Capacity for Bus Stops Using Queuing Theory and Diffusion Approximation

Modeling Bus Capacity for Bus Stops Using Queuing Theory and Diffusion Approximation

Risk Area Identification Model of Bus Bay Stops Based on Distribution of Conflicts

Assessing the Risk of Bus Crashes in Transit Systems

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