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%

Efficient Journey Planning and Congestion Prediction Through Deep Learning

The advancements of technology continuously rising over the years has seen many applications that are useful in providing users with sufficient information to make better journey plans on their own. However, commuters still find themselves going through congested routes every day to get to their destinations. This paper attempts to delineate the possibilities of improving urban mobility through big data processing and deep-learning models. Essentially, through a predictive model to predict congestion and its duration, this paper aims to develop and validate a functional journey planning mobile application that can predict traffic conditions, allowing road users to make better informed decisions to their travel plans. This paper proposes a Multi-Layered Perceptron (MLP) deep learning model for congestion prediction and supplements a Linear Regression (LR) model to predict its duration. The proposed MLP-LR model performed reasonably well with an accuracy of 63% in predicting an occurrence of congestion. Some critical discussions on further research opportunities stemming from this study is also presented

Prediction feedback in intelligent traffic systems

The optimal information feedback has a significant effect on many socioeconomic systems like stock market and traffic systems aiming to make full use of resources. In this paper, we studied dynamics of traffic flow with real-time information provided and the influence of a feedback strategy named prediction feedback strategy is introduced, based on a two-route scenario in which dynamic information can be generated and displayed on the board to guide road users to make a choice. Our model incorporates the effects of adaptability into the cellular automaton models of traffic flow and simulation results adopting this optimal information feedback strategy have demonstrated high efficiency in controlling spatial distribution of traffic patterns compared with the other three information feedback strategies, i.e., vehicle number and flux.Comment: 14 pages, 15 figure

Effects of Prediction Feedback in Multi-Route Intelligent Traffic Systems

We first study the influence of an efficient feedback strategy named prediction feedback strategy (PFS) based on a multi-route scenario in which dynamic information can be generated and displayed on the board to guide road users to make a choice. In this scenario, our model incorporates the effects of adaptability into the cellular automaton models of traffic flow. Simulation results adopting this optimal information feedback strategy have demonstrated high efficiency in controlling spatial distribution of traffic patterns compared with the other three information feedback strategies, i.e., vehicle number and flux. At the end of this paper, we also discuss in what situation PFS will become invalid in multi-route systems.Comment: 15 pages, 15 figures, Physica A (2010), doi:10.1016/j.physa.2010.02.03