ST TD outlier detection

Sun, Dihua; Zhao, Hongzhuan; Yue, Hang; Zhao, Min; Cheng, Shuying; Han, Wei

doi:10.1049/iet-its.2016.0261

Cited by 18 publications

(9 citation statements)

References 25 publications

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“…Noisy data can influence the output of all models, to some extent. Therefore, it is highly recommended in many studies to apply data cleansing methods before going to analysing step [62]- [66].…”

Section: A Datamentioning

confidence: 99%

Simulation-Based Sensitivity Analysis for Evaluating Factors Affecting Bus Service Reliability: A Big and Smart Data Implementation

et al. 2020

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Service quality is a significant concern for both providers and users of public transportation. It is crucial for transit agencies to clearly recognize the causes of unreliability before adapting any improvement strategy. However, evaluation of main causes of bus service unreliability has not been investigated well. Existing studies have three main limitations in context of recognizing causes of service unreliability. First, public transport networks and traffic condition are highly complex systems and most of the existing models are not capable to accurately determine the relationship between service irregularity and impact factors. Second, definition of "Big data" has been neglected and most of the studies only focused on one source of large scale data set to determine the causes of unreliability. Third, bus service unreliability can impact the users" perception toward the public transport, significantly. It has been recommended by number of studies that bus service reliability should be evaluated from both service providers" and users" perspective. However, the impact of service unreliability from passengers" perception is not well investigated, yet. Consequently, we proposed a novel simulation-based sensitivity analysis to evaluating main causes of bus service unreliability using a combination of three different sources of big data. Moreover, for the first time we developed a simulation model in R studio which is an open source and powerful coding environment. According to the results, the level of reliability in Route U32 showed the highest sensitivity to headway variations. Waiting time can be decreased by 61% if only bus operators can reduce the headway variation by 25% of the actual observed data. Big gap and bus bunching could be almost disappeared by decreasing headway variations. Moreover, the terminal departure policy could significantly improve the passenger waiting time. Waiting time can be decreased by 36% when almost all the buses depart the terminal on-time.

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Section: A Datamentioning

confidence: 99%

Simulation-Based Sensitivity Analysis for Evaluating Factors Affecting Bus Service Reliability: A Big and Smart Data Implementation

et al. 2020

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“…Based on such method, the underlying relationship between traffic flow and crash may be better explored. us, active regional traffic control and management strategies [11][12][13][14][15] could be expected to improve macroscopic safety, by managing traffic flow in some certain state with lower crash risk.…”

Section: Introductionmentioning

confidence: 99%

Developing a New Spatial Unit for Macroscopic Safety Evaluation Based on Traffic Density Homogeneity

Wang

Liu

2020

Journal of Advanced Transportation

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Macrolevel crash modeling has been extensively applied to investigate the safety effects of demographic, socioeconomic, and land use factors, in order to add safety knowledge into traffic planning and policy-making. In recent years, with the increasing attention to regional traffic management and control, the safety effects of macrolevel traffic flow parameters may also be of interest, in order to provide useful safety knowledge for regional traffic operation. In this paper, a new spatial unit was developed using a recursive half-cut partitioning procedure based on a normalized cut (NC) minimization method and traffic density homogeneity. Two Bayesian lognormal models with different conditional autoregressive (CAR) priors were applied to examine the safety effects of traffic flow characteristics at the NC level. It was found that safety effects of traffic flow exist at such macrolevel, indicating the necessity of considering safety for regional traffic control and management. Furthermore, traffic flow effects were also examined for another two spatial units: Traffic Analysis Zone (TAZ) and Census Tract (CT). It was found that ecological fallacy and atomic fallacy could exist without considering traffic flow parameters at those planning-based levels. In general, safety needs to be considered for regional traffic operation and the effects of traffic flow need to be considered for spatial crash modeling at various spatial levels.

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“…Thus, the detection of traffic outliers is vital for traffic management to detect unusual incidents and help department of transportation make smart decisions. Thus, the traffic outlier detection has become a hot topic in transportation research [3, 4]. Some traffic outlier detection systems are built from loop counter data, which need careful maintenance.…”

Section: Introductionmentioning

confidence: 99%

Spatial–temporal traffic outlier detection by coupling road level of service

Chen

et al. 2019

IET Intelligent Transport Systems

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ST TD outlier detection

Cited by 18 publications

References 25 publications

Simulation-Based Sensitivity Analysis for Evaluating Factors Affecting Bus Service Reliability: A Big and Smart Data Implementation

Simulation-Based Sensitivity Analysis for Evaluating Factors Affecting Bus Service Reliability: A Big and Smart Data Implementation

Developing a New Spatial Unit for Macroscopic Safety Evaluation Based on Traffic Density Homogeneity

Spatial–temporal traffic outlier detection by coupling road level of service

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