Utilizing bluetooth and adaptive signal control data for real-time safety analysis on urban arterials

Abstract

Real-time safety analysis has become a hot research topic as it can more accurately reveal the relationships between real-time traffic characteristics and crash occurrence, and these results could be applied to improve active traffic management systems and enhance safety performance. Most of the previous studies have been applied to freeways and seldom to arterials. This study attempts to examine the relationship between crash occurrence and realtime traffic and weather characteristics based on four urban arterials in Central Florida. Considering the substantial difference between the interrupted urban arterials and the access controlled freeways, the adaptive signal phasing data was introduced in addition to the traditional traffic data. Bayesian conditional logistic models were developed by incorporating the Bluetooth, adaptive signal control, and weather data, which were extracted for a period of 20 minutes (four 5-minute intervals) before the time of crash occurrence. Model comparison results indicated that the model based on 5-10 minute interval dataset performs the best. It revealed that the average speed, upstream left-turn volume, downstream green ratio, and rainy indicator were found to have significant effects on crash occurrence. Furthermore, both Bayesian random parameters logistic and Bayesian random parameters conditional logistic models were developed to compare with the Bayesian conditional logistic model, and the Bayesian random parameters conditional logistic model was found to have the best model performance in terms of the AUC and DIC values. These results are important in real-time safety applications in the context of Integrated Active Traffic Management.

Current Understanding of the Effects of Congestion on Traffic Accidents

“…Of particular interest is the use of real-time traffic measurement technologies, which can identify unsafe traffic conditions as they are developing, allowing measures to be taken to alleviate these conditions [19]. Bluetooth traffic sensors are one method used to collect this high temporal resolution traffic data which can be used for measuring crash risk, such as in studies by Yuan, et al, [20], Yuan, et al, [21], and Yuan and Abdel-Aty [22]. Studies investigating the effects of traffic and weather on traffic accidents are generally focused on freeways and urban expressways, with urban areas being less examined [23].…”

“…Studies investigating the effects of traffic and weather on traffic accidents are generally focused on freeways and urban expressways, with urban areas being less examined [23]. While sources of real-time traffic data such as remote traffic microwave sensors (RTMS) and automated vehicle identification (AVI) systems are often available on highway systems, Bluetooth sensors can be used to measure traffic conditions in urban locations [20]. This highlights Bluetooth technology as a possible method for filling the gap in urban studies.…”

“…This highlights Bluetooth technology as a possible method for filling the gap in urban studies. Although, in Yuan’s studies in Florida [20,21,22], Bluetooth detectors were used for collecting travel speed data, while 15-min traffic volume data was collected using adaptive signal controllers. This poses the question as to whether it would be viable to use Bluetooth detectors as the sole source of traffic data, using travel time as a proxy for levels of congestion; a potential direction for future development and use of this new technology.…”

From Sensors Data to Urban Traffic Flow Analysis

“…For implementing an effective mobility, these data must be up-to-date, and in many cases, real-time information. In order to obtain this information there are two main possibilities: employ crowdsourced data that monitor citizen movements, using GPS (usually taxi GPS trajectories data are employed as in [10]) and Bluetooth data from mobile phones (as in [11] and [12]) or take advantage of traffic sensor data using them as input for a traffic simulation model.…”

“…An overview of the model structure is depicted in Figure 4. SUMO (Simulation of Urban Mobility) 11 is an open-source, highly portable, microscopic and continuous road traffic simulation package and is designed to handle large road networks. This simulation suite has been continuously developed for more than 15 years and has been extensively successfully applied in different projects related to urban traffic management, traffic emission and other traffic issues [29].…”