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%

DxNAT - Deep Neural Networks for Explaining Non-Recurring Traffic Congestion

Non-recurring traffic congestion is caused by temporary disruptions, such as accidents, sports games, adverse weather, etc. We use data related to real-time traffic speed, jam factors (a traffic congestion indicator), and events collected over a year from Nashville, TN to train a multi-layered deep neural network. The traffic dataset contains over 900 million data records. The network is thereafter used to classify the real-time data and identify anomalous operations. Compared with traditional approaches of using statistical or machine learning techniques, our model reaches an accuracy of 98.73 percent when identifying traffic congestion caused by football games. Our approach first encodes the traffic across a region as a scaled image. After that the image data from different timestamps is fused with event- and time-related data. Then a crossover operator is used as a data augmentation method to generate training datasets with more balanced classes. Finally, we use the receiver operating characteristic (ROC) analysis to tune the sensitivity of the classifier. We present the analysis of the training time and the inference time separately

Managing Urban Mobility during Big Events through Living Lab Approach

Urban transportation systems encounter distinctive challenges during planned major city events characterized by large gatherings that disrupt traffic patterns. The surge in private car usage for attending such events leads to a sudden increase in traffic, unauthorized parking, pollutant emissions, and risks to pedestrian safety in the vicinity of the event venue. This study delves into the challenges and advantages of employing Decision Support Systems (DSSs) to manage urban mobility during special urban events with the goal of reducing car dependency and promoting sustainable transportation options. The proposed methodology for designing and testing the DSS is based on the living lab principles of co-planning, co-implementing, co-monitoring, co-validating, and co-reviewing with engaged stakeholders. Moreover, testing of the DSS measures in real-world cases (i.e., during a football match at the San Siro Stadium and a concert at the Alcatraz music hall in the city of Milan, Italy) highlights the potential of the DSS in reducing the use of individual private cars in favor of shared mobility and micro-mobility solutions. As a result, the living lab has proven to be a valuable tool for interacting with stakeholders from the outset of brainstorming ideas for potential transport policies to their practical implementation, with the goal of bridging the gap between what decision-makers believe should be done, what transport operators can feasibly do, and what users desire and expect to be done. The insights presented in this paper contribute to the debate on leveraging technology to cultivate more efficient, resilient, and livable urban environments

A Data Driven Approach to Quantify the Impact of Crashes

The growth of data has begun to transform the transportation research and policy, and open a new window for analyzing the impact of crashes. Currently for the crash impact analysis, researchers tend to rely on reported incident duration, which may not always be accurate. Further, impact of the crashes could linger a much longer time at upstream, even if the records are correct for the crash spot and it is a challenge to quantify the impact of a crash from the complex dynamics of the recurrent and non-recurrent congested condition. Therefore, a difference-in-speed approach is developed in this research to estimate the true crash impact duration using stationary sensor data and incident logs. The proposed method used the Kalman filter algorithm to establish traveler’s anticipated travel speed under incident-free condition and then employ the difference-in-speed approach to quantify the temporal and spatial extent of the crash. Moreover, potential applications such as statistical models for predicting the impact duration and total delay were developed in this research. Later, an analysis on distribution of travel rate was performed to describe and numerically show to what extent crashes influenced travel rates compared with the normal conditions at different periods of the day and by the crash types. This study can help to shape incident management policies for different types of crashes at different periods and illustrates the usages of data to improve the understanding of crashes, their impact, and their distribution in a spatial-temporal domain

Generating demand responsive bus routes from social network data analysis

Acknowledgment The research reflected in this paper has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 770115.Peer reviewedPostprin

A Smartphone-Based System for Outdoor Data Gathering Using a Wireless Beacon Network and GPS Data: From Cyber Spaces to Senseable Spaces

Information and Communication Technologies (ICTs) and mobile devices are deeply influencing all facets of life, directly affecting the way people experience space and time. ICTs are also tools for supporting urban development, and they have also been adopted as equipment for furnishing public spaces. Hence, ICTs have created a new paradigm of hybrid space that can be defined as Senseable Spaces. Even if there are relevant cases where the adoption of ICT has made the use of public open spaces more “smart”, the interrelation and the recognition of added value need to be further developed. This is one of the motivations for the research presented in this paper. The main goal of the work reported here is the deployment of a system composed of three different connected elements (a real-world infrastructure, a data gathering system, and a data processing and analysis platform) for analysis of human behavior in the open space of Cardeto Park, in Ancona, Italy. For this purpose, and because of the complexity of this task, several actions have been carried out: the deployment of a complete real-world infrastructure in Cardeto Park, the implementation of an ad-hoc smartphone application for the gathering of participants’ data, and the development of a data pre-processing and analysis system for dealing with all the gathered data. A detailed description of these three aspects and the way in which they are connected to create a unique system is the main focus of this paper.This work has been supported by the Cost Action TU1306, called CYBERPARKS: Fostering knowledge about the relationship between Information and Communication Technologies and Public Spaces supported by strategies to improve their use and attractiveness, the Spanish Ministry of Economy and Competitiveness under the ESPHIA project (ref. TIN2014-56042-JIN) and the TARSIUS project (ref. TIN2015-71564-C4-4-R), and the Basque Country Department of Education under the BLUE project (ref. PI-2016-0010). The authors would also like to thank the staff of UbiSive s.r.l. for the support in developing the application