A Cyberinfrastructure for BigData Transportation Engineering

Big Data-driven transportation engineering has the potential to improve utilization of road infrastructure, decrease traffic fatalities, improve fuel consumption, decrease construction worker injuries, among others. Despite these benefits, research on Big Data-driven transportation engineering is difficult today due to the computational expertise required to get started. This work proposes BoaT, a transportation-specific programming language, and it's Big Data infrastructure that is aimed at decreasing this barrier to entry. Our evaluation that uses over two dozen research questions from six categories show that research is easier to realize as a BoaT computer program, an order of magnitude faster when this program is run, and exhibits 12-14x decrease in storage requirements

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%

Exploring spatio-temporal effects in traffic crash trend analysis

Unobserved heterogeneity produced by spatial and temporal correlations of crashes often needs to be captured in crash frequency modeling. Although many studies have included either spatial or temporal effects in crash frequency modeling, only a limited number of studies have considered both. This study addresses the limitations of existing studies by exploring multiple models that best fit the spatial and temporal correlations. In this study, we used Bayesian spatio-temporal models to investigate regional crash frequency trends, and explored the effects of omitting spatial or temporal trends in spatio-temporal correlated data. The fast Bayesian inference approach, integrated nested Laplace approximation, was used to estimate parameters. It was found that fatal crashes showed decreasing trends in all Iowa counties from 2006 to 2015, but the decreasing rates varied by counties. Among all the covariates investigated, only vehicle miles traveled (VMT) was significant. None of the socio-economic or weather indicators were found to be significant in the presence of VMT. Both spatial and temporal effects were found to be important, and they were responsible for both over dispersion and zero inflation in the crash data. In addition, spatial effects played a more important role than did temporal effects in the studied dataset, but temporal component selection was still important in spatio-temporal modeling

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

A deep‐learning‐based computer vision solution for construction vehicle detection

This paper aims at providing researchers and engineering professionals from the first step of solution development to the last step of solution deployment with a practical and comprehensive deep‐learning‐based solution for detecting construction vehicles. This paper places particular focus on the often‐ignored last step of deployment. Our first phase of solution development involved data preparation, model selection, model training, and model validation. Given the necessarily small‐scale nature of construction vehicle image datasets, we propose as detection model an improved version of the single shot detector MobileNet, which is suitable for embedded devices. Our study\u27s second phase comprised model optimization, application‐specific embedded system selection, economic analysis, and field implementation. Several embedded devices were proposed and compared. Results including a consistent above 90% mean average precision confirm the superior real‐time performance of our proposed solutions. Finally, the practical field implementation of our proposed solutions was investigated. This study validates the practicality of deep‐learning‐based object detection solutions for construction scenarios. Moreover, the detailed information provided by the current study can be employed for several purposes such as safety monitoring, productivity assessments, and managerial decision making

LCCA-based Decision Assistance Tool for Indirect Left Turn (ILT) Intersections using Excel-driven Highway Capacity Software

This paper explains the principles involved in the development of an MS Excel - based decision assistance tool for indirect left (ILT) intersections. this tool, termed Signalized Intersection Life Cycle Cost Analysis (SILCC), analyzes three types of ILT intersections; (i) MUt, (ii) CFl, and (iii) jughandles. SO far, no tools have been developed that are capable of analyzing ILT intersections while incorporating cost and benefit aspects. In contrast, SILCC is designed to incorporate cost and benefit aspects in the evaluation of ILT intersections. It is interfaced with the Highway Capacity Software (HCS) and hence can perform macro-level operational analysis. It considers delay, fuel consumption, and emissions as operational performance measures. It is capable of performing life cycle cost analysis (LCCA) and providing net present value (NPV) and benefit-to-cost ratio (B/C) as surrogate measures of performance. Planners can use NPV or B/C for decision support while deciding among several alternatives for economic and efficiently operating ILT intersections. Additionally, SILCC feature the flexibility to alter input values so that it can be used for multiple conditions and criteria. A case study of rual traffic volume conditions indication that a MUT intersection had the highest NPV of benefits for both new construction and retrofits. However, because the construction cost for MUT retrofits was high for the particular condition, an MUT intersection had the highest B/C for new construction an a jughandle had the highest B/C for retrofits