Modelling Reliability of Transportation Systems to Reduce Traffic Congestion

Reliability reflects the level of ease of people and goods to travel. Reliability relates with the variability of travel time, speed, and system usage and transportation system capacity. The higher the capacity of the system and the faster the travel time, the higher the level of reliability of the transportation system, so that it can reduce traffic congestion. Traffic congestion will occur when travel requests exceed road capacity. Our research attempts to provides a comprehensive and objective assessment of improving reliability of transportation systems and its impact to reduce traffic congestion. We utilized system dynamics simulation model to test and evaluate the alternatives of future strategies to increase the reliability of transportation systems and its impact to reduce traffic congestion. Systems dynamics models can be developed at the macroscopic and microscopic levels of transportation systems as well as to evaluate the effects of different transportation policies on traffic congestion. Simulation results show that reliability is determined by several factors such as travel time, headway (the time between two means of transportation to pass a point / place), passenger wait time; access time and egress time (the time needed to get off the vehicle when it arrives at the destination). Transportation systems reliability continues to decline so that in 2017, reliability was only around 41.5%. The improvement scenario of transportation system reliability can be done by conducting several strategies such as increasing road capacity, increasing public vehicle routes around public facilities, as well as increasing the supply of public transportation which has an impact on headway and waiting time. By conducting these strategies, the reliability of transportation systems could be increased to be around 53.3% and a gradual increase in road capacity could be done with a growth of 2.8% per year. Within this condition, the traffic congestion is projected to be around 74.9% -83.6% in the period 2019-2027, and then to be around 83-88% in the period 2028-2035

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

Predicting Traffic Congestion in Presence of Planned Special Events

The recent availability of datasets on transportation networks with high spatial and temporal resolution is enabling new research activities in the fields of Territorial Intelligence and Smart Cities. Within these domains, in this paper we focus on the problem of predicting traffic congestion in urban environments caused by attendees leaving a Planned Special Events (PSE), such as a soccer game or a concert. The proposed approach consists of two steps. In the first one, we use the K-Nearest Neighbor algorithm to predict congestions within the vicinity of the venue (e.g. a Stadion) based on the knowledge from past observed events. In the second step, we identify the road segments that are likely to show congestion due to PSEs and map our prediction to these road segments. To visualize the traffic trends and congestion behavior we learned and to allow Domain Experts to evaluate the situation we also provide a Google Earth-based GUI. The proposed solution has been experimentally proven to outperform current state of the art solutions by about 35% and thus it can successfully serve to reliably predict congestions due to PSEs