An Online Decision-Theoretic Pipeline for Responder Dispatch

The problem of dispatching emergency responders to service traffic accidents, fire, distress calls and crimes plagues urban areas across the globe. While such problems have been extensively looked at, most approaches are offline. Such methodologies fail to capture the dynamically changing environments under which critical emergency response occurs, and therefore, fail to be implemented in practice. Any holistic approach towards creating a pipeline for effective emergency response must also look at other challenges that it subsumes - predicting when and where incidents happen and understanding the changing environmental dynamics. We describe a system that collectively deals with all these problems in an online manner, meaning that the models get updated with streaming data sources. We highlight why such an approach is crucial to the effectiveness of emergency response, and present an algorithmic framework that can compute promising actions for a given decision-theoretic model for responder dispatch. We argue that carefully crafted heuristic measures can balance the trade-off between computational time and the quality of solutions achieved and highlight why such an approach is more scalable and tractable than traditional approaches. We also present an online mechanism for incident prediction, as well as an approach based on recurrent neural networks for learning and predicting environmental features that affect responder dispatch. We compare our methodology with prior state-of-the-art and existing dispatch strategies in the field, which show that our approach results in a reduction in response time with a drastic reduction in computational time.Comment: Appeared in ICCPS 201

Using Imaging Technology to Evaluate Highway Safety

Crash-based safety analysis is set back by several shortcomings such as randomness and rarity of crash occurrences, lack of timeliness, and inconsistency in crash reporting. Non-crash-based safety analysis has been around for more than three decades but its potential was limited due to a difficulty in the data collection and the evaluation. Recent advancement in digital videos and image detection technology renewed our interest in facilitating the data collection and improving the evaluation method. Two image detection systems for the measurement of traffic characteristics were evaluated: (a) a commercial video detection system and (b) proprietary image processing software. The measurement evaluation revealed that both systems were still not sufficiently accurate for the safety evaluation purpose and thus a manual measurement from digitized video clips was preferred for a collection of evaluation data. We proposed a novel application of extreme value theory to safety evaluation based on observable traffic characteristics. The proposed method was evaluated by applying to right-angle collisions at signalized intersections. A traffic characteristic so-called post-encroachment time (PET) was collected at selected intersections as a surrogate safety measure. Based on PET characteristics, risk and frequency of rightangle crashes at the studied intersection or individual conflict zone can be estimated using only the data collected at the location. For comparison, a traditional approach to safety analysis using Poisson and negative binomial regression analyses was also examined. Both evaluation methods – extreme value approach and regression – indicate a significant relationship between PETs and historical crash data. Simulation experiments were carried out to examine the effect of observation period on a variance of estimates obtained the proposed method. Advantages and problems with the proposed method are described in this study. A simple method for an evaluation of the risk of right-angle collisions at signalized intersections is also provided in the appendix

Innovative non-crash-based safety estimation: An extreme value theory approach

Crash-based safety analysis is hampered by several shortcomings, such as randomness and rarity of crash occurrences, lack of timeliness, and inconsistency in crash reporting. Safety analysis based on observable traffic characteristics more frequent than crashes is one promising alternative. A traditional approach to alternative safety analysis relies on the assumption of constant risk across locations. In addition, the current practice of collecting surrogate data often suffers from the inherent subjectivity of the humans involved in the task. In this research, we propose a novel application of the extreme value theory to a non-crash-based safety estimation that no longer relies on the assumption of constant risk. We evaluate the proposed method by applying it to right-angle collisions at signalized intersections. The feasibility of facilitating the measurement of traffic characteristics with digital video and image processing technology is also examined. Eight-hour traffic movements at selected intersections were recorded using a mobile traffic laboratory. The risk of right-angle collisions was estimated using so-called post-encroachment times (PET). Evaluated video image processing techniques were not sufficiently accurate for the purpose of our research. Therefore, post-processing of digitized video clips using a manual method was selected. The Poisson and negative binomial regression analyses of short PETS and observed crash counts indicate a significant relationship between these two. A series of negated PET observations was discretized into fixed time intervals and the maximum values in each interval were treated as extremes. This approach elegantly handles the dependence of extremes in comparison to an alternative approach that defines threshold excesses as extremes. A distribution of extreme values was modeled with a generalization of the generalized extreme value distribution as the non-stationary r largest order statistic model. Based on the premise that PETS being zero or less define a collision situation, safety levels were determined from the model in terms of crash frequency and return level estimates. Evaluation of the safety estimates against historical crash counts indicates a promising relationship between these two. However, the proposed method still yields large-variance estimates due to an insufficient observation period. A semi-empirical simulation experiment revealed that a few weeks of PET observation were needed to obtain crash frequency estimates with confidence intervals comparable to those being obtained from three-year observed crash counts. The proposed method can be applied to other types of locations and collisions as well

Queuing simulation of roadside survey station: Blocked traffic lane

External station travel surveys provide critical inputs to travel demand models. The results from these models are frequently used for statewide planning purposes. Although a roadside survey is very effective in obtaining useful information from road users, its major drawback is the excessive delay that is imposed onto road users particularly on high-volume facilities. In this paper, we used a discrete event simulation to model a blocked traffic lane survey, which is usually conducted for two-lane undivided highways. This type of survey station requires a complete stop of all oncoming traffic. Non-surveyed traffic has no ability to go around and thus has to wait in a queue in order to proceed through the survey station. Road users' impacts are quantified in terms of delay and queue length while the performance of surveyors is measured by the number of surveys completed per unit time. Sensitivity analyses of simulation inputs reveal that simulation results are fairly insensitive to selected parameters. The results in this study provide a quick and useful guideline that roadside surveyors can use to estimate the road user impacts prior to the survey and to plan the survey procedure accordingly.

Research report (Southwest Region University Transportation Center (U.S.))

Report on connected vehicles, reviewing current research and technologies and developing near-term practical applications that use connected vehicle technology

Technical report (Texas Transportation Institute)

"This document summarized the research conducted and the conclusions reached during the development of guidelines for pedestrian safety treatments at signalized intersections. The guidelines are focused on treatments that alleviate conflicts between left-turning vehicles and pedestrians.

Technical report (Texas Transportation Institute)

Report regarding a portable computer that interfaces with traffic signal cabinets, monitors and logs events that occur within the cabinet, and then analyzes the log files and creates easy to read reports

A Real-time Transit Signal Priority Control System that Considers Stochastic Bus Arrival Times

Transit Signal Priority (TSP) is an effective strategy for providing preferential treatment to move transit vehicles through intersections with minimum delay. However, TSP can disrupt traffic on non-priority phases if not properly implemented. To produce a good TSP strategy, advance planning with enough lead time is usually preferred; this means added uncertainty about the bus arrival at the stop bar, which has been difficult to be accounted for. Researchers proposed a stochastic mixed-integer nonlinear model (SMINP) to be used as the core component of a real-time transit signal priority control system. The SMINP was implemented in a simulation evaluation platform. An analysis was performed to compare the proposed control model with the standard check-in/check-out TSP system implemented in the VISSIM Built-in Ring-Barrier Controller (RBC-TSP). The results showed the SMINP produced as much as 30 percent improvement of bus delay from the RBC-TSP in low to medium volume conditions. In high-volume conditions, the SMINP model automatically recognizes the level of congestion of the intersection and gives less priority to the bus so as to maintain a minimum impact to the traffic on its conflicting phases. In the case of multiple conflicting bus lines, a rolling optimization scheme was developed. A comparison indicated the RBC-TSP systems cannot handle a high degree of saturation when there are significant amount of conflicts between bus lines, while the SMINP can automatically give less priority to bus so as to cause much less impact to other traffic

Technical report (Texas Transportation Institute)

"This project focused primarily on the specific issue of developing a new technical tool to help TxDOT and other key operating agencies/stakeholders better predict when major elements of evacuation operations should be implemented.

Technical report (Texas Transportation Institute)

Report of a project to identify and evaluate methods that may reduce the number of sign hit by errant vehicles with field studies conducted near Corpus Christi, Texas