Unobserved heterogeneity across space, time, and crash type is often non-negligible in crash frequency modeling. When multiple crash types with spatial and temporal features are analyzed, multivariate spatiotemporal models should be considered. For this study, we analyzed the yearly county-level fatal, major injury, and minor injury crashes in Iowa from 2006 to 2015 using a multivariate spatio-temporal Bayesian model. The model adopted a multivariate spatial structure, a multivariate temporal structure, and a multivariate spatiotemporal interaction structure to account for possible correlations across injury severities over space, time, and spatio-temporal interaction, respectively. Income and weather indicators were found to have no significant effects on crash frequencies in the presence of vehicle miles traveled and unemployment rate. Both spatial and temporal effects were found to be important, and they played nearly the same roles for all three crash types in the studied dataset. Counties located in north and southwest Iowa were found to tend to have fewer crashes than the remaining counties. All three crash types generally showed descending trends from 2006 to 2015. They also had significantly positive correlations between each other in space but not in time. The crude crash rates and predicted crash rates were generally consistent for major injury and minor injury crashes but not for low-count fatal crashes. High-risk counties were identified using the posterior expected rank by the predicted crash cost rate, which was more able to truly represent the underlying traffic safety status than the rank by the crude crash cost rate.

This supplementary document shows detailed proofs of the theoretical results in the main paper and the implementation of the bivariate spline smoothing over the triangulation. In Section B.1, we investigate the asymptotic properties of the oracle estimator, the first stage penalized spline pilot estimators, and the spline-backfitted local polynomial estimator. Section B.2 describes the implementation of bivariate spline. Section B.3 provides more simulation results from Examples 1

Urban midblock crashes are influenced mainly by traffic operation and roadway geometric features. In this paper, 10-year crash data from 1,506 directional urban midblock segments in Nebraska were analyzed using the multivariate random parameters zero-inflated negative binomial model to account for unobserved heterogeneity produced by correlations across segments, correlations across crash collision types, excessive zero crashes, and over dispersion. The multivariate random parameters zero-inflated negative binomial model was superior to many common crash frequency models in terms of both goodness of fit and prediction accuracy. Compared with the multivariate fixed parameters zero-inflated negative binomial model, the multivariate random parameters zero-inflated negative binomial model identified fewer key influencing factors and revealed segment-specific effects of these factors on different crash types. It showed that the number of lanes, annual average daily traffic per lane, and segment length might have non-positive effects on crash frequencies. Segments with a speed limit of 45 mph had fewer crashes than did those with lower speed limits, and there were fewer crashes on the segments in Omaha than on those in Lincoln. It was also found that neither the presence of a shoulder, on-street parking, or one-way traffic, nor lane width had significant influences on crash frequencies. These findings are informative for transportation agencies to take correct and efficient measures to accommodate diverse transportation demands without reducing traffic safety. By contrast, the fixed parameters model produced results consistent with intuition, but the results were insufficient to provide actionable recommendations.

Unobserved heterogeneity produced by spatial and temporal correlations of crashes often needs to be captured in crash frequency modeling. Although many studies have included either spatial or temporal effects in crash frequency modeling, only a limited number of studies have considered both. This study addresses the limitations of existing studies by exploring multiple models that best fit the spatial and temporal correlations. In this study, we used Bayesian spatio-temporal models to investigate regional crash frequency trends, and explored the effects of omitting spatial or temporal trends in spatio-temporal correlated data. The fast Bayesian inference approach, integrated nested Laplace approximation, was used to estimate parameters. It was found that fatal crashes showed decreasing trends in all Iowa counties from 2006 to 2015, but the decreasing rates varied by counties. Among all the covariates investigated, only vehicle miles traveled (VMT) was significant. None of the socioeconomic or weather indicators were found to be significant in the presence of VMT. Both spatial and temporal effects were found to be important, and they were responsible for both over dispersion and zero inflation in the crash data. In addition, spatial effects played a more important role than did temporal effects in the studied dataset, but temporal component selection was still important in spatio-temporal modeling.

Electric Vehicles (EVs), by reducing the dependency on fossil fuel and minimizing the traffic-related pollutants emission, are considered as an effective component of a sustainable transportation system. However, the massive penetration of EVs brings a big challenge to the establishment of charging infrastructures. This paper presents the approach to locate charging stations utilizing the reconstructed EVs trajectory derived from the Cellular Signaling Data (CSD). Most previous work focused on the commute trips estimated from the number of jobs and households between traffic analysis zones (TAZs). This paper investigated the large-scale CSD and illustrated the method to generate the 24-hour travel demand for each EV. The complete trip in a day for EV was reconstructed through merging the time sequenced trajectory derived from simulation. This paper proposed a two-step model that grouped the charging demand location into clusters and then identified the charging station site through optimization. The proposed approach was applied to investigate the charging behavior of medium-range EVs with Cellular Signaling Data collected from the China Unicom in Tianjin. The results indicate that over 50% of the charging stations are located within the central urban area. The developed approach could contribute to the planning of future charging stations.

By using the 2017 National Household Travel Survey (NHTS) data, this study explores the status quo of ownership and usage of conventional vehicles (CVs) and alternative fuel vehicles (AFVs), i.e., Hybrid Electric Vehicles (HEVs), Plug-in Hybrid Electric Vehicles (PHEVs) and Battery Electric Vehicles (BEVs), in the United States. The young ages of HEVs (6.0 years), PHEVs (3.2 years) and BEVs (3.1 years) demonstrate the significance of the 2017 NHTS data. The results show that after two decades of development, AFVs only occupy about 5% of annual vehicle sales, and their share does not show big increases in recent years. Meanwhile, although HEVs still dominate the AFV market, the share of PHEVs & BEVs has risen to nearly 50% in 2017. In terms of ownership, income still seems to be a major factor influencing AFV adoption, with the median annual household incomes of CVs, HEVs, PHEVs and BEVs being $75,000, $100,000, $150,000 and $200,000, respectively. Besides, AFV households are more likely to live in urban areas, especially large metropolitan areas. Additionally, for AFVs, the proportions of old drivers are much smaller than CVs, indicating this age group might still have concerns regarding adopting AFVs. In terms of travel patterns, the mean and 85th percentile daily trip distances of PHEVs and HEVs are significantly larger than CVs, followed by BEVs. BEVs might still be able to replace CVs for meeting most travel demands after a single charge, considering most observed daily trip distances are fewer than 93.5 km for CVs. However, the observed max daily trip distances of AFVs are still much smaller than CVs, implying increasing the endurance to meet extremely long-distance travel demands is pivotal for encouraging consumers to adopt AFVs instead of CVs in the future.

Many studies investigate contributing factors of intersection crashes, but very limited studies focus on cashes on the intersection approach. It is important to address the characteristics of intersection-approach crashes to better understand intersection safety. This article analyzes the crashes on signalized intersection approach on urban arterials with a focus on traffic and geometric elements. The intersection approach is defined as the segment between stop bar and the location 200 ft upstream from the stop bar. The multivariate Poisson lognormal (MVPLN) model is used to model crash counts by severity. Ten-year crash data collected from 643 signalized intersections in Nebraska are used for analysis. One-way road is found to be negatively related to all three severity levels (light crash, moderate crash, and severe crash) of crashes. Compared to the 12 ft lane width, narrower lane widths generally lead to fewer crashes. The intersection approaches on urban arterials are expected to have more crashes than collector roads. The numbers of right-turn, left-turn, and through lanes, as well as the annual average daily traffic on the intersection approach and its crossing approach are statistically significant factors increasing crash frequency. The MVPLN model is compared to univariate and zero-inflated Poisson models. The results reveal that the MVPLN model provides a superior fit over the univariate Poisson model. KeywordsMultivariate Poisson log-normal, signalized intersection, crash analysis, traffic safety, intersection approach ABSTRACTMany studies investigate contributing factors of intersection crashes, but few focus on crashes on intersection approaches. It is important to address the characteristics of intersection approach crashes to better understand intersection safety. This paper analyzes the crashes on signalized intersection approaches on urban arterials with a focus on traffic and geometric elements. The intersection approach is defined as the segment between the stop bar and 200 ft upstream, and the multivariate Poisson-lognormal (MVPLN) model is used to model crash counts by severity. Ten-year crash data, collected from 643 signalized intersections in Nebraska, are analyzed. It was found that one-way roads negatively impact all three crash severity levels (light, moderate, and severe), and compared to the 12 ft lane width, narrower lane widths generally lead to fewer crashes. The intersection approaches on urban arterials are expected to have more crashes than collector roads. The amount of right-turn, left-turn, and through lanes, and the annual average daily traffic (AADT) on the intersection approach and its crossing approach are statistically significant factors increasing crash frequency. The MVPLN model is compared to univariate and zero-inflated Poisson models. The results reveal that the MVPLN model provides a superior fit compared to the univariate Poisson model.

With the rapid development of technologies, the ecological control strategies of connected and autonomous vehicle (CAV) technologies are gaining more and more attention. In this paper, a rule-based ecological cruise control, called the ecological smart driver model (EcoSDM), is proposed to improve the fuel efficiency of an individual vehicle and the traffic flow. By adjusting the spacing between the leading and the following vehicles, EcoSDM provides smoother deceleration and acceleration than the enhanced intelligent driver model (Enhanced-IDM) and the smart driver model (SDM). The linear stability of EcoSDM is analyzed both theoretically and numerically. The numerical results validate the results of theoretical analysis. Moreover, the simulations results show that EcoSDM outperforms the Enhanced-IDM and SDM in terms of stabilization effect on homogeneous traffic flow. In addition, the calibrated VT-Micro model is used to estimate the fuel consumption of CAVs and manually driven vehicles. The result shows that CAVs have better fuel economy than the human-driven vehicles, which is consistent with existing studies. The EcoSDM outperforms Enhanced-IDM and SDM in terms of fuel consumption. For the EcoSDM-equipped CAVs, the fuel saving benefit is greatest when a CAV is at the front of the platoon. INDEX TERMS Connected and autonomous vehicle (CAV), ecological adaptive cruise control, linear stability.