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%

Big data driven assessment of probe-sourced data

Presently, there is an expanding interest among transportation agencies and state Departments of Transportation to consider augmenting traffic data collection with probe-based services, such as INRIX. The objective is to decrease the cost of deploying and maintaining sensors and increase the coverage under constrained budgets. This dissertation documents a study evaluating the opportunities and challenges of using INRIX data in Midwest. The objective of this study is threefold: (1) quantitative analysis of probe data characteristics: coverage, speed bias, and congestion detection precision (2) improving probe based congestion performance metrics accuracy by using change point detection, and (3) assessing the impact of game day schedule and opponents on travel patterns and route choice. The first study utilizes real-time and historical traffic data which are collected through two different data sources; INRIX and Wavetronix. The INRIX probe data stream is compared to a benchmarked Wavetronix sensor data source in order to explain some of the challenges and opportunities associated with using wide area probe data. In the following, INRIX performance is thoroughly evaluated in three major criteria: coverage and penetration, speed bias, congestion detection precision. The second study focuses on the number of congested events and congested hour as two important performance measures. To improve the accuracy and reliability of performance measures, this study addresses a big issue in calculating performance measures by comparing Wavetronix against INRIX. We examine the very traditional and common method of congestion detection and congested hour calculation which utilized a fixed-threshold and we show how unreliable and erroneous that method can be. After that, a novel traffic congestion identification method is proposed in this paper and in the following the number of congested events and congested hour are computed as two performance measures. After evaluating the accuracy and reliability of INRIX probe data in chapter 2 and 3, the purpose of the last study in chapter 4 is to assess the impacts of game day on travel pattern and route choice behaviors using INRIX, the accurate and reliable data source. It is shown that the impacts vary depending on the schedule and also the opponents. Also, novel methods are proposed for hotspot detection and prediction. Overall, this dissertation evaluates probe-sourced streaming data from INRIX, to study its characteristics as a data source, challenges and opportunities associated with using wide area probe data, and finally make use of INRIX as a reliable data source for travel behavior analysis

Evaluation of Opportunities and Challenges of Using INRIX Data for Real-Time Performance Monitoring and Historical Trend Assessment

In recent years there has been a growing desire for the use of probe vehicle technology for congestion detection and general infrastructure performance assessment. Unlike costly traditional data collection by loop detectors, wide-area detection using probe-sourced traffic data is significantly different in terms of measurement technique, pricing, coverage, etc. This affects how the new technology is applied and used to solve current traffic problems such as traffic incident management and roadway performance assessment. This report summarizes the experiences and lessons learned while using probe data for traffic operations and safety management in the state of Nebraska and makes recommendations for opportunities to maximize the use of probe data in light of its limitations. A detailed analysis of performance monitoring and historical trend analysis, including identification of the top 10 congested segments, congestion per mile across metro areas, congested hour(s) during summer and winter months, and yearly travel time reliability, for Interstate 80 segments in Nebraska were performed. Two main conclusions can be drawn from this study. First, there is almost always a speed bias between data streaming from probes and traditional infrastructure-mounted sensors. It is important to understand the factors that influence these biases and how to cope with them. Second, lack of confidence score 30 (real-time) probe data is a critical issue that should be considered precisely for incident detection, roadway performance assessment, travel time estimation, and other traffic analyses. Ultimately, the authors present several recommendations that will help transportation agencies gain the best value from their probe data

Big data driven assessment of probe-sourced data

Presently, there is an expanding interest among transportation agencies and state Departments of Transportation to consider augmenting traffic data collection with probe-based services, such as INRIX. The objective is to decrease the cost of deploying and maintaining sensors and increase the coverage under constrained budgets. This dissertation documents a study evaluating the opportunities and challenges of using INRIX data in Midwest. The objective of this study is threefold: (1) quantitative analysis of probe data characteristics: coverage, speed bias, and congestion detection precision (2) improving probe based congestion performance metrics accuracy by using change point detection, and (3) assessing the impact of game day schedule and opponents on travel patterns and route choice. The first study utilizes real-time and historical traffic data which are collected through two different data sources; INRIX and Wavetronix. The INRIX probe data stream is compared to a benchmarked Wavetronix sensor data source in order to explain some of the challenges and opportunities associated with using wide area probe data. In the following, INRIX performance is thoroughly evaluated in three major criteria: coverage and penetration, speed bias, congestion detection precision. The second study focuses on the number of congested events and congested hour as two important performance measures. To improve the accuracy and reliability of performance measures, this study addresses a big issue in calculating performance measures by comparing Wavetronix against INRIX. We examine the very traditional and common method of congestion detection and congested hour calculation which utilized a fixed-threshold and we show how unreliable and erroneous that method can be. After that, a novel traffic congestion identification method is proposed in this paper and in the following the number of congested events and congested hour are computed as two performance measures. After evaluating the accuracy and reliability of INRIX probe data in chapter 2 and 3, the purpose of the last study in chapter 4 is to assess the impacts of game day on travel pattern and route choice behaviors using INRIX, the accurate and reliable data source. It is shown that the impacts vary depending on the schedule and also the opponents. Also, novel methods are proposed for hotspot detection and prediction. Overall, this dissertation evaluates probe-sourced streaming data from INRIX, to study its characteristics as a data source, challenges and opportunities associated with using wide area probe data, and finally make use of INRIX as a reliable data source for travel behavior analysis.</p

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%

Evaluation of Opportunities and Challenges of Using INRIX Data for Real-Time Performance Monitoring and Historical Trend Assessment

In recent years there has been a growing desire for the use of probe vehicle technology for congestion detection and general infrastructure performance assessment. Unlike costly traditional data collection by loop detectors, wide-area detection using probe-sourced traffic data is significantly different in terms of measurement technique, pricing, coverage, etc. This affects how the new technology is applied and used to solve current traffic problems such as traffic incident management and roadway performance assessment. This report summarizes the experiences and lessons learned while using probe data for traffic operations and safety management in the state of Nebraska and makes recommendations for opportunities to maximize the use of probe data in light of its limitations. A detailed analysis of performance monitoring and historical trend analysis, including identification of the top 10 congested segments, congestion per mile across metro areas, congested hour(s) during summer and winter months, and yearly travel time reliability, for Interstate 80 segments in Nebraska were performed. Two main conclusions can be drawn from this study. First, there is almost always a speed bias between data streaming from probes and traditional infrastructure-mounted sensors. It is important to understand the factors that influence these biases and how to cope with them. Second, lack of confidence score 30 (real-time) probe data is a critical issue that should be considered precisely for incident detection, roadway performance assessment, travel time estimation, and other traffic analyses. Ultimately, the authors present several recommendations that will help transportation agencies gain the best value from their probe data.This report is published as Sharma, Anuj, Vesal Ahsani, and Sandeep. “Evaluation of Opportunities and Challenges of Using INRIX Data for Real-Time Performance Monitoring and Historical Trend Assessment.” Report no. SPR-P1(14) M007. Nebraska Department of Roads. 2017. Posted with permission.</p

Traffic Congestion Detection from Camera Images using Deep Convolution Neural Networks

Recent improvements in machine vision algorithms have led to closed-circuit television (CCTV) cameras emerging as an important data source for determining of the state of traffic congestion. In this study we used two different deep learning techniques, you only look once (YOLO) and deep convolution neural network (DCNN), to detect traffic congestion from camera images. The support vector machine (SVM), a shallow algorithm, was also used as a comparison to determine the improvements obtained using deep learning algorithms. Occupancy data from nearby radar sensors were used to label congested images in the dataset and for training the models. YOLO and DCCN achieved 91.5% and 90.2% accuracy, respectively, whereas SVM’s accuracy was 85.2%. Receiver operating characteristic curves were used to determine the sensitivity of the models with regard to different camera configurations, light conditions, and so forth. Although poor camera conditions at night affected the accuracy of the models, the areas under the curve from the deep models were found to be greater than 0.9 for all conditions. This shows that the models can perform well in challenging conditions as well.This is a manuscript of an article published as Chakraborty, Pranamesh, Yaw Okyere Adu-Gyamfi, Subhadipto Poddar, Vesal Ahsani, Anuj Sharma, and Soumik Sarkar. "Traffic congestion detection from camera images using deep convolution neural networks." Transportation Research Record 2672, no. 45 (2018): 222-231. DOI: 10.1177%2F0361198118777631. Posted with permission.</p