Kun Xie

Corridor-level signalized intersection safety analysis in Shanghai, China using Bayesian hierarchical models

Most traffic crashes in Chinese cities occur at signalized intersections. Research on the intersection safety problem in China is still in its early stage. The recent development of an advanced traffic information system in Shanghai enables in-depth intersection safety analyses using road design, traffic operation, and crash data. In Shanghai, the road network density is relatively high and the distance between signalized intersections is small, averaging about 200m. Adjacent signalized intersections located along the same corridor share similar traffic flows, and signals are usually coordinated. Therefore, when studying intersection safety in Shanghai, it is essential to account for intersection correlations within corridors. In this study, data for 195 signalized intersections along 22 corridors in the urban areas of Shanghai were collected. Mean speeds and speed variances of corridors were acquired from taxis equipped with Global Positioning Systems (GPS). Bayesian hierarchical models were applied to identify crash risk factors at both the intersection and the corridor levels. Results showed that intersections along corridors with lower mean speeds were associated with fewer crashes than those with higher speeds, and those intersections along two-way roads, under elevated roads, and in close proximity to each other, tended to have higher crash frequencies.

Spatial analysis of highway incident durations in the context of Hurricane Sandy

The objectives of this study are (1) to develop an incident duration model which can account for the spatial dependence of duration observations, and (2) to investigate the impacts of a hurricane on incident duration. Highway incident data from New York City and its surrounding regions before and after Hurricane Sandy was used for the study. Morans I statistics confirmed that durations of the neighboring incidents were spatially correlated. Moreover, Lagrange Multiplier tests suggested that the spatial dependence should be captured in a spatial lag specification. A spatial error model, a spatial lag model and a standard model without consideration of spatial effects were developed. The spatial lag model is found to outperform the others by capturing the spatial dependence of incident durations via a spatially lagged dependent variable. It was further used to assess the effects of hurricane-related variables on incident duration. The results show that the incidents during and post the hurricane are expected to have 116.3% and 79.8% longer durations than those that occurred in the regular time. However, no significant increase in incident duration is observed in the evacuation period before Sandys landfall. Results of temporal stability tests further confirm the existence of the significant changes in incident duration patterns during and post the hurricane. Those findings can provide insights to aid in the development of hurricane evacuation plans and emergency management strategies.

Work Zone Safety Analysis and Modeling: A State-of-the-Art Review

Objective: Work zone safety is one of the top priorities for transportation agencies. In recent years, a considerable volume of research has sought to determine work zone crash characteristics and causal factors. Unlike other non–work zone–related safety studies (on both crash frequency and severity), there has not yet been a comprehensive review and assessment of methodological approaches for work zone safety. To address this deficit, this article aims to provide a comprehensive review of the existing extensive research efforts focused on work zone crash-related analysis and modeling, in the hopes of providing researchers and practitioners with a complete overview. Methods: Relevant literature published in the last 5 decades was retrieved from the National Work Zone Crash Information Clearinghouse and the Transport Research International Documentation database and other public digital libraries and search engines. Both peer-reviewed publications and research reports were obtained. Each study was carefully reviewed, and those that focused on either work zone crash data analysis or work zone safety modeling were identified. The most relevant studies are specifically examined and discussed in the article. Results: The identified studies were carefully synthesized to understand the state of knowledge on work zone safety. Agreement and inconsistency regarding the characteristics of the work zone crashes discussed in the descriptive studies were summarized. Progress and issues about the current practices on work zone crash frequency and severity modeling are also explored and discussed. The challenges facing work zone safety research are then presented. Conclusions: The synthesis of the literature suggests that the presence of a work zone is likely to increase the crash rate. Crashes are not uniformly distributed within work zones and rear-end crashes are the most prevalent type of crashes in work zones. There was no across-the-board agreement among numerous papers reviewed on the relationship between work zone crashes and other factors such as time, weather, victim severity, traffic control devices, and facility types. Moreover, both work zone crash frequency and severity models still rely on relatively simple modeling techniques and approaches. In addition, work zone data limitations have caused a number of challenges in analyzing and modeling work zone safety. Additional efforts on data collection, developing a systematic data analysis framework, and using more advanced modeling approaches are suggested as future research tasks.

Using Big Data to Study Resilience of Taxi and Subway Trips for Hurricanes Sandy and Irene

Hurricanes Irene and Sandy had a significant impact on New York City; the result was devastating damage to the New York City transportation systems, which took days, even months to recover. This study explored posthurricane recovery patterns of the roadway and subway systems of New York City on the basis of data for taxi trips and for subway turnstile ridership. Both data sets were examples of big data with millions of individual ridership records per month. The spatiotemporal variations of transportation system recovery behavior were investigated by using neighborhood tabulation areas as units of analysis. Recovery curves were estimated for each evacuation zone category to model time-dependent recovery patterns of the roadway and subway systems. The recovery rate for Hurricane Sandy was found to be lower than that for Hurricane Irene. In addition, the results indicate a higher resilience of the road network compared with the subway network. The methodology proposed in this study can be used to evaluate the resilience of transportation systems with respect to natural disasters and the findings can provide government agencies with useful insights into emergency management.

Modeling Evacuation Behavior Under Hurricane Conditions

The understanding of evacuation behavior is critical to establishing policies, procedures, and organizational structure for an effective response to emergencies. This study specifically investigated the evacuation behavioral responses under hurricane conditions. The study aimed to explore the association between contributing factors and the evacuation decision choices as well as evacuation destination choices. Unlike previous studies that modeled each response behavior separately, this study proposed to use the structural equation modeling approach to examine the interrelationship between response behaviors. A case study was performed with the data set from a survey conducted in New Jersey. With Bayesian estimation approaches, the proposed structural equation models were estimated, and the effect of each predictive variable was captured. An important finding is that individuals’ preference to evacuate did not significantly affect their choices of evacuation destinations. In addition, other socioeconomic and demographic characteristics that affected evacuation behavior were identified.

Development of Online Scalable Approach for Identifying Secondary Crashes

Secondary crashes are some of the most critical incidents occurring on highways. Such crashes can induce extra traffic delays and affect highway safety performance. Transportation agencies are interested in understanding the mechanism of the occurrence of secondary crashes and implementing appropriate countermeasures. However, no well-established procedure identifies secondary crashes; this deficiency in turn impedes the possibility of investigating the underlying mechanism of their occurrence. The intent of this study was to develop an online scalable approach for helping to identify secondary crashes for the large number of highways with insufficient traffic surveillance units to collect the continuous traffic data required to classify such crashes accurately. The developed approach consisted of two major components: (a) acquisition of open source traffic data and (b) identification of secondary crashes through the use of these data. Unlike existing approaches based on static thresholds, queuing models, or infrastructure-based sensor data, the developed approach took advantage of various open-source data to identify traffic conditions in the presence of incidents. This study proposed to develop virtual sensors collecting traffic data from private traffic information providers such as Bing Maps, Google Maps, and MapQuest. The availability of such data greatly expands the ability of transportation agencies to cover more highways without installing infrastructure sensors. The virtual-sensor output provides the basic input to run the developed automatic identification algorithm for identifying secondary crashes. The algorithm is described step by step to provide a readily deployable approach for transportation agencies interested in identifying secondary crashes on their highway networks.

Systematic Approach to Hazardous-Intersection Identification and Countermeasure Development

Safety performance functions (SPFs) are typically used to correlate geometric, traffic and environmental characteristics with total crashes and to identify hotspots which have high overall crash frequencies. However, with a distinct conflict pattern in vehicle maneuvers, each crash type is likely to associate with different risk factors. This study developed approach-level SPFs using a full Bayesian method to assess the safe effects of specific risk factors for rear-end, left-turn, right-angle, sideswipe and total crashes. To account for the spatial correlations among approaches at the same intersection, a random intersection-specific effect term was incorporated into each model. It was affirmed that these models were helpful in identifying high risk intersections with specific safety problems, and could serve as useful complements to general hotspot analyses using expected crash totals. In addition, it was found that certain variables (e.g. number of through lanes, median, and left-turn protection all on the entering approach) could have even contrary effects on crash occurrence of different types. Approach-level crash type models provide valuable insights in developing countermeasures aimed at reducing certain crash types and an improved ability in identifying deficiencies related to geometric and traffic characteristics for each intersection approach.

Secondary collisions and injury severity: A joint analysis using structural equation models

ABSTRACT Objective: This study aims to investigate the contributing factors to secondary collisions and the effects of secondary collisions on injury severity levels. Manhattan, which is the most densely populated urban area of New York City, is used as a case study. In Manhattan, about 7.5% of crash events become involved with secondary collisions and as high as 9.3% of those secondary collisions lead to incapacitating and fatal injuries. Methods: Structural equation models (SEMs) are proposed to jointly model the presence of secondary collisions and injury severity levels and adjust for the endogeneity effects. The structural relationship among secondary collisions, injury severity, and contributing factors such as speeding, alcohol, fatigue, brake defects, limited view, and rain are fully explored using SEMs. In addition, to assess the temporal effects, we use time as a moderator in the proposed SEM framework. Results: Due to its better performance compared with other models, the SEM with no constraint is used to investigate the contributing factors to secondary collisions. Thirteen explanatory variables are found to contribute to the presence of secondary collisions, including alcohol, drugs, inattention, inexperience, sleep, control disregarded, speeding, fatigue, defective brakes, pedestrian involved, defective pavement, limited view, and rain. Regarding the temporal effects, results indicate that it is more likely to sustain secondary collisions and severe injuries at night. Conclusions: This study fully investigates the contributing factors to secondary collisions and estimates the safety effects of secondary collisions after adjusting for the endogeneity effects and shows the advantage of using SEMs in exploring the structural relationship between risk factors and safety indicators. Understanding the causes and impacts of secondary collisions can help transportation agencies and automobile manufacturers develop effective injury prevention countermeasures.

Data-Driven Spatial Modeling for Quantifying Networkwide Resilience in the Aftermath of Hurricanes Irene and Sandy

In recent years, the New York City metropolitan area was hit by two major hurricanes, Irene and Sandy. These extreme weather events disrupted and devastated the transportation infrastructure, including road and subway networks. As an extension of the authors’ recent research on this topic, this study explored the spatial patterns of infrastructure resilience in New York City with the use of taxi and subway ridership data. Neighborhood tabulation areas were used as the units of analysis. The recovery curve of each neighborhood tabulation area was modeled with the logistic function to quantify the resilience of road and subway systems. Morans I tests confirmed the spatial correlation of recovery patterns for taxi and subway ridership. To account for this spatial correlation, citywide spatial models were estimated and found to outperform linear models. Factors such as the percentage of area influenced by storm surges, the distance to the coast, and the average elevation are found to affect the infrastructure resilience. The findings in this study provide insights into the vulnerability of transportation networks and can be used for more efficient emergency planning and management.

Robust vehicle tracking for urban traffic videos at intersections

We develop a robust, unsupervised vehicle tracking system for videos of very congested road intersections in urban environments. Raw tracklets from the standard Kanade-Lucas-Tomasi tracking algorithm are treated as sample points and grouped to form different vehicle candidates. Each tracklet is described by multiple features including position, velocity, and a foreground score derived from robust PCA background subtraction. By considering each tracklet as a node in a graph, we build the adjacency matrix for the graph based on the feature similarity between the tracklets and group these tracklets using spectral embedding and Dirichelet Process Gaussian Mixture Models. The proposed system yields excellent performance for traffic videos captured in urban environments and highways.