Assessing the Impact of Game Day Schedule and Opponents on Travel Patterns and Route Choice using Big Data Analytics

The transportation system is crucial for transferring people and goods from point A to point B. However, its reliability can be decreased by unanticipated congestion resulting from planned special events. For example, sporting events collect large crowds of people at specific venues on game days and disrupt normal traffic patterns. The goal of this study was to understand issues related to road traffic management during major sporting events by using widely available INRIX data to compare travel patterns and behaviors on game days against those on normal days. A comprehensive analysis was conducted on the impact of all Nebraska Cornhuskers football games over five years on traffic congestion on five major routes in Nebraska. We attempted to identify hotspots, the unusually high-risk zones in a spatiotemporal space containing traffic congestion that occur on almost all game days. For hotspot detection, we utilized a method called Multi-EigenSpot, which is able to detect multiple hotspots in a spatiotemporal space. With this algorithm, we were able to detect traffic hotspot clusters on the five chosen routes in Nebraska. After detecting the hotspots, we identified the factors affecting the sizes of hotspots and other parameters. The start time of the game and the Cornhuskers’ opponent for a given game are two important factors affecting the number of people coming to Lincoln, Nebraska, on game days. Finally, the Dynamic Bayesian Networks (DBN) approach was applied to forecast the start times and locations of hotspot clusters in 2018 with a weighted mean absolute percentage error (WMAPE) of 13.8%

Impact of Signal Timing Information on Safety and Efficiency of Signalized Intersections

Signalized intersections are provided in traffic networks to improve the safety and efficiency of vehicular and pedestrian movement. There are various measures under education, enforcement and engineering headings that are being attempted to improve safety and efficiency of operations at a signalized intersection. Provision of signal countdown timer, a timer showing the remaining red and green time in a phase, is one such measure and is commonly adopted in India. However, studies on effects of countdown timer under Indian traffic conditions are very scarce. Traffic heterogeneity and lack of lane discipline makes transferability of models developed in other countries (with more organized traffic) infeasible. The present study is an attempt to analyze the changes in queue discharge characteristics and red light violations (RLV) under Indian traffic conditions due to the presence of timer. A before and after analysis was carried out using the data collected from a selected intersection in Chennai, India. The analysis is carried out for different vehicle types in the presence and absence of timers separately for the start and end of red/green. Results showed that the information provided at the start of green (end of red) enhances efficiency, the startup lost time is reduced and there is an increase in red light violations. Two wheelers present at the start of the queue are found to be the category that is mostly affected by this information. However, the information provided at end of green (start of red) was found to reduce the red light violations. In the presence of information, it was found that the propensity of RLV (proportion of cycles having RLV) reduced from 59 % to 31 % at the end of green (start of red) and there was an increase from 12 % to 75 % at the start of green (end of red) with statistically significant drop in the headways (indicating an increased efficiency). Also, in presence of information, the intensity of RLV (Mean RLVs per RLV cycle) for both start of red and end of red reduced from 3.32 to 2.30 vehicles and 8.52 to 5.60 vehicles respectively. The impacts varied based on the vehicle types with major impacts on the two wheelers. The queue discharge models show a significant change in trend implying a need to update the signal timings when the timer’s are installed. These results also bring into light the trade-off between safety and efficiency and the choices drivers make in the presence of phase change information. These trade-offs should be carefully considered as the technology advances and drivers are provided more and more information. For example, with the advent of intellidrive technology (vehicle to infrastructure communications), the extent of information provided to the drivers should be tailored to achieve system optimality and results from studies such as the present one can help in decision making

Use of Decision Assistance Curves in Advanced Warrant Analysis for Indirect Left-Turn Intersections

This paper develops decision assistance curves (DAC) to compare delay-based performance measures for three indirect left turn (ILT) intersections, namely median U-turn (MUT), continuous flow intersection (CFI), and jughandle, relative to a conventional signalized intersection. The DACs consist of two graphical tools: (i) DAC-classifier and (ii) two sets of DAC-contours. DAC-classifier plots are used to select the intersection type that produces the minimum system average delay for a specified main and cross-street volume configuration. DAC-contour plots are used to estimate the system average delay difference between a chosen ILT and a conventional signalized intersection as well as to estimate the increase in average delay as compared to a conventional signalized intersection for the most negatively impacted movement. These tools can be used by planners, engineers, or other decision makers to visually identify the intersection type that provides the least average system delay under given volume conditions as well as estimated tradeoffs for choosing a specific intersection type. It was found that the conventional signalized intersection, with protected left turns, was never optimal under studied scenarios. This implies that, for all the studied conditions, there exists at least one ILT or permitted left turn alternative that produces lower delay than the conventional signalized intersection

Traffic System Anomaly Detection using Spatiotemporal Pattern Networks

Traffic dynamics in the urban interstate system are critical in terms of highway safety and mobility. This paper proposes a systematic data mining technique to detect traffic system-level anomalies in a batch-processing fashion. Built on the concepts of symbolic dynamics, a spatiotemporal pattern network (STPN) architecture is developed to capture the system characteristics. This novel spatiotemporal graphical modeling approach is shown to be able to extract salient time series features and discover spatial and temporal patterns for a traffic system. An information-theoretic metric is used to quantify the causal relationships between sub-systems. By comparing the structural similarity of the information-theoretic metrics of the STPNs learnt from each day, a day with anomalous system characteristics can be identified. A case study is conducted on an urban interstate in Iowa, USA, with 11 roadside radar sensors collecting 20-second resolution speed and volume data. After applying the proposed methods on one-month data (Feb. 2017), several system-level anomalies are detected. The potential causes that include inclement weather condition and non-recurring congestion are also verified to demonstrate the efficacies of the proposed technique. Compared to the traditional predefined performance measures for the traffic systems, the proposed framework has advantages in capturing spatiotemporal features in a fast and scalable manner