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

Taxi drivers' traffic violations detection using random forest algorithm: A case study in China

OBJECTIVE

To effectively explore the impacts of several key factors on taxi drivers' traffic violations and provide traffic management departments with scientific decisions to reduce traffic fatalities and injuries.

METHODS

43,458 electronic enforcement data about taxi drivers' traffic violations in Nanchang City, Jiangxi Province, China, from July 1, 2020, to June 30, 2021, were utilized to explore the characteristics of traffic violations. A random forest algorithm was used to predict the severity of taxi drivers' traffic violations and 11 factors affecting traffic violations, including time, road conditions, environment, and taxi companies were analyzed using the Shapley Additionality Explanation (SHAP) framework.

RESULTS

Firstly, the ensemble method Balanced Bagging Classifier (BBC) was applied to balance the dataset. The results showed that the imbalance ratio (IR) of the original imbalanced dataset reduced from 6.61% to 2.60%. Moreover, a prediction model for the severity of taxi drivers' traffic violations was established by using the Random Forest, and the results showed that accuracy, m_F1, m_G-mean, m_AUC, and m_AP obtained 0.877, 0.849, 0.599, 0.976, and 0.957, respectively. Compared with the algorithms of Decision Tree, XG Boost, Ada Boost, and Neural Network, the performance measures of the prediction model based on Random Forest were the best. Finally, the SHAP framework was used to improve the interpretability of the model and identify important factors affecting taxi drivers' traffic violations. The results showed that functional districts, location of the violation, and road grade were found to have a high impact on the probability of traffic violations; their mean SHAP values were 0.39, 0.36, and 0.26, respectively.

CONCLUSIONS

Findings of this paper may help to discover the relationship between the influencing factors and the severity of traffic violations, and provide a theoretical basis for reducing the traffic violations of taxi drivers and improving the road safety management.

Bayesian spatial correlation, heterogeneity and spillover effect modeling for speed mean and variance on urban road networks

Analyzing speed mean and variance is vital to safety management in urban roadway networks. However, modeling speed mean and variance on structured roads could be influenced by the spatial effects, which are rarely addressed in the existing studies. The inadequacy may lead to biased conclusions when considering vehicle speed as a surrogate safety measure. The current study focuses on developing a Bayesian modeling approach with three types of spatial effects, i.e., spatial correlation, spatial heterogeneity, and spillover effect. To capture the spatial correlation, the study employs the intrinsic conditional autoregressive (ICAR) models, spatial lag models (SLM), and spatial error models (SEM). Spatial heterogeneity and spillover effect are considered by the random parameters approach and spatially lagged covariates (SLCs). Speed data are collected from the float cars running on 134 urban arterials in Chengdu, China. The results indicate that the random parameters ICAR model with SLCs (RPICAR-SLC) outperforms others in terms of goodness-of-fit, accuracy, and efficiency for modeling speed mean, while the random parameters ICAR model (RPICAR) is the best for modeling speed variance. Moreover, RPICAR-SLC and RPICAR models are beneficial to address spatial correlation of residuals, explaining the unobserved influence among the observations, and are less likely to cause biased or overestimated parameters. The study also discusses how traffic conditions, road characteristics, traffic management strategies, and facilities on roadway networks influence speed mean and variance. The findings highlight the importance of multi-type spatial effects on modeling speed mean and variance along the structured roadways.

Evaluation of the effect of fixed speed cameras on speeding behavior among Iranian taxi drivers through telematics monitoring

OBJECTIVE

Installation of speed cameras is a common strategy to reduce over-speeding; however, there is evidence that their efficacy in speed reduction is limited to the proximity of the camera. This study aimed to evaluate driving speeds in relation to the position of cameras among Iranian taxi drivers.

METHODS

Speed data were collected from April 2020 to January 2021 via telematics devices (using on-board computer, gyroscope, and GPS) installed on taxis in southern Tehran, Iran. All drivers were males above 20 years of age. Throughout the study, taxi drivers were not changed. Eligible road segments were selected based on: a) not containing any obstacle that would cause speed reduction; b) having ≤5 entry/exit points; c) absence of park and ride or taxi stations; and d) availability of at least 5,000 datapoints. The average speed was compared between the camera- and non-camera zones. Camera zone was defined as the area within 300 meters of the speed cameras.

RESULTS

The telematics system included 2,644,846 datapoints gathered from 50 taxis. Two highways' segments with three lanes in each direction were included: Tehran-Varamin (18 taxis, 18,978 datapoints) and Ghadir (17 taxis, 8,203 datapoints). On both highways, speed was significantly lower in the camera zones (Tehran-Varamin: 84.9 ± 12.2 km/h versus 86.7 ± 13.7 km/h; P = 0.005; Kolmogorov-Smirnov test (KS) P < 0.001/Ghadir: 68.7 ± 13.7 versus 73.1 ± 11.3; P = 0.008; KS P < 0.001), indicating a V-shaped distribution of speed near the position of cameras (Presence of Kangaroo effect). Drivers were more likely to exceed speed limits in the non-camera zones compared to camera zones (Tehran-Varamin: 14.6% versus 8.4%/Ghadir: 23.1% versus 17.3%). This effect of the cameras was consistently observed in a subgroup analysis based on time of day (daytime versus nighttime).

CONCLUSIONS

Among Iranian taxi drivers in southern Tehran, average speed was significantly lower in the vicinity of speed cameras, suggesting the presence of camera manipulation. Alternative speed control interventions are required to improve the safety of the taxi service.

Operational factor analysis of the aggressive taxi speeders using random parameters Bayesian LASSO modeling approach

Partial taxi speeders are observed with both high speeding frequency and severity (range). They thereby can be viewed as aggressive speeders whose behaviors may result in more hazards than others. Among the factors contributing to taxi speeding, the operational factors are proven to be deterministic. However, previous studies mainly investigate the operational factors of taxi speeding frequency, which fail to comprehensively unveil the impact of factors on speeders, especially for aggressive speeders. This study intends to disclose the operational factors affecting the aggressive taxi speeders with the random parameters Bayesian least absolute shrinkage and selection operator (LASSO) modeling approach. Taxi speeding behaviors and several operational factors are extracted from taxi GPS trajectory data in Chengdu, China. Based on the hourly speeding frequency and average speeding severity of each speeder, the fuzzy C-means clustering algorithm is employed to categorize taxi speeders into three cohorts: restrained speeder (RS), moderate speeder (MS), and belligerent speeder (BS). Compared to RS, MS and BS are treated as the aggressive taxi speeders. Several binary logistic models are developed with RS as the reference category. The random parameters Bayesian binary logistic LASSO model that captures the unobserved heterogeneity and tackles the multicollinearity is found to be the best fit model to identify the significant operational factors. The results indicate that aggressive taxi speeders are linked to longer daily driving distance and cruise distance, shorter delivery time, higher hourly income, driving at night, and driving on low-speed limit roads. However, intensive lane-changes and sufficient daily naps do not contribute to aggressive taxi speeders. Moreover, BS is more sensitive to the operational factors than MS. This study stresses the necessity of implementing speeder classification in taxi driver management and conceiving countermeasures considering the operational factors which are significantly associated with the aggressive taxi speeders.