Spatial Analysis of Taxi Speeding Event Using GPS Trajectory Data

Fu, Chuanyun; Zhou, Yue; Xu, Chuan; Cui, Haipeng

doi:10.1109/itsc.2019.8916870

Cited by 10 publications

(10 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The increase in crash frequency at the higher posted speed limit is expected since increased speeds are known to entail a higher risk of a crash. In that regard, several studies have demonstrated the positive exponential relationship between the speed limit and crashes ( 37 , 68–70 ). In alignment with this study, the previous study of Alhomaidat et al ( 71 ) indicated a 13.9 percent increase in arterial road crash frequency as a result of 5 mph increase in adjacent freeway speed limits.…”

Section: Discussionmentioning

confidence: 98%

“…Despite that, there are a few exemplary works that significantly contributed to the study of spatial dependence of traffic crash data. These include the auto-Poisson model ( 32 ), the two-step estimator for the spatial lag of counts ( 33 ), the Poisson spatial interaction model with spatial effects ( 34 ), the Gibbs sampler approach for spatial Poisson regression models ( 35 ), the accurate likelihood evaluation of a broad class of spatial models ( 36 ), spatial analysis of taxi speeding ( 37 ), and a three spatial effect Bayesian modeling for speed mean and variance ( 18 ).…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Segment-Level Spatial Spillover Effects of Exogenous Characteristics of Arterials on Crash Frequency

Atumo

Zhang²,

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

Studies of spatial effects on road traffic safety are highly skewed in relation to aggregation units. As a result, the spatial effect of road traffic crashes at segment-level aggregation is rarely investigated. In light of that, this study investigates spatial spillover effects to determine the effects of independent variable changes initiated in one segment on the dependent variable of its neighboring segments. Additionally, the performance of the implemented models is evaluated under five different spatial weighting approaches. Spatial spillover effect of fatal and injury crashes aggregated at the segment level is estimated with the spatial lag of explanatory variables model under the general framework of Poisson and negative binomial models. The results indicate (i) spatial spillover effects are better modeled with spatial relation conceptualized by inverse distance spatial weighting schemes; (ii) spatial lag of explanatory variables under negative binomial and inverse distance spatial weighting provides the best model fitting; (iii) exogenous interactions among influence factors of fatal and injury crashes have a noteworthy effect in determining the crash frequency of neighboring segments. Convincingly, spatial spillover effect seems to significantly affect the results of conventional count modeling at the segment levels. To that effect, this study expects to provide empirical evidence and complement the existing literature on the impact of changes in explanatory variables initiated in a given segment on the safety of segments in proximity.

show abstract

Section: Discussionmentioning

confidence: 98%

Section: Literature Reviewmentioning

confidence: 99%

Segment-Level Spatial Spillover Effects of Exogenous Characteristics of Arterials on Crash Frequency

Atumo

Zhang²,

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…Spatial correlation effect refers to as a phenomenon that sample values are interactive due to their continuity of geography. For example, road in business district is likely to be associated with higher speeding frequency [13], which may cause high speeding rate on its surrounding roads. Spatial heterogeneity effect presents the discrepancy of sample value over the study area due to the geographic difference.…”

Section: Methodsmentioning

confidence: 99%

“…Meanwhile, the reliability of those data is easily impacted by environment and traffic condition. To overcome the data bias, several researches used GPS trajectory data to examine taxi speeding events [11,13]. These studies showed the extensive usage of GPS is able to capture a large amount of vehicle trajectory data.…”

Section: Introductionmentioning

confidence: 99%

“…For example, the studies used Bayesian model with spatial effect to estimate the traffic crash with errors caused by spatial component, raising the fitness and explanatory ability of the crash contributor [14][15][16][17]. As to speeding research, an latest study held by Fu et al [13] have identified the existence of spatial autocorrelation of taxi speeding events with different severity level. However, the introduction of spatial effect is unseen in previous researches [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian hierarchical spatial count modeling of taxi speeding events based on GPS trajectory data

Liu

Jiang

et al. 2020

PLoS ONE

Self Cite

View full text Add to dashboard Cite

Speeding behavior, especially serious speeding, is more common in taxi driver than other driving population due to their high exposure under traffic environment, which increases the risk of being involved in crashes. In order to prevent the taxi and other road users from speed-related crash, previous studies have revealed contributors of demographic and driving operation affecting taxi speeding frequency. However, researches regarding road factors, and spatial effect are typically rare. For this sake, the current study explores the contributions of 10 types of road characteristics and two kinds of spatial effects (spatial correlation and spatial heterogeneity) on taxi total speeding and serious speeding frequency. Taxi GPS trajectory data in a Chinese metropolis were used to identify speeding event. The study then established four kinds of Bayesian hierarchical count models base on Poisson and negative binominal distribution to estimate the contributor impacts, respectively. Results show that Bayesian hierarchical spatial Poisson log-linear model is optimum for fitting both total and serious speeding frequency. For the analysis, it is found that drivers are more likely to commit speeding on long multilane road with median strip, and road with non-motorized vehicle lane, bus-only lane and viaduct or road tunnel. Roads with low speed limit, and work zone are associated with increasing speeding as well. In terms of serious speeding, bus-only lane is not a contributor, while road speed camera number and one-way organization are significantly positive to the speeding frequency. Furthermore, it reveals that two spatial effects significantly increase the occurrence of speeding events; the impact of spatial heterogeneity is more critical.

show abstract

Investigating Contributing Factors of Hard-Braking Events on Urban Road Network

Zhou

Liu

2022

Smart Innovation, Systems and Technologies

View full text Add to dashboard Cite

Spatial Analysis of Taxi Speeding Event Using GPS Trajectory Data

Cited by 10 publications

References 24 publications

Segment-Level Spatial Spillover Effects of Exogenous Characteristics of Arterials on Crash Frequency

Segment-Level Spatial Spillover Effects of Exogenous Characteristics of Arterials on Crash Frequency

Bayesian hierarchical spatial count modeling of taxi speeding events based on GPS trajectory data

Investigating Contributing Factors of Hard-Braking Events on Urban Road Network

Contact Info

Product

Resources

About