A Multi-Contextual Approach to Modeling the Impact of Critical Highway Work Zones in Large Urban Corridors

Accurate Construction Work Zone (CWZ) impact assessments of unprecedented travel inconvenience to the general public are required for all federally-funded highway infrastructure improvement projects. These assessments are critical, but they are also very difficult to perform. Most existing prediction approaches are project-specific, shortterm, and univariate, thus incapable of benchmarking the potential traffic impact of CWZs for highway construction projects. This study fills these gaps by creating a big-data-based decision-support framework and testing if it can reliably predict the potential impact of a CWZ under arbitrary lane closure scenarios. This study proposes a big-data-based decision-support analytical framework, “Multi-contextual learning for the Impact of Critical Urban highway work Zones” (MICUZ). MICUZ is unique as it models the impact of CWZ operations through a multi-contextual quantitative method utilizing sensored big transportation data. MICUZ was developed through a three-phase modeling process. First, robustness of the collected sensored data was examined through a Wheeler’s repeatability and reproducibility analysis, for the purpose of verifying the homogeneity of the variability of traffic flow data. The analysis results led to a notable conclusion that the proposed framework is feasible due to the relative simplicity and periodicity of highway traffic profiles. Second, a machine-learning algorithm using a Feedforward Neural Networks (FNN) technique was applied to model the multi-contextual aspects of iii long-term traffic flow predictions. The validation study showed that the proposed multi-contextual FNN yields an accurate prediction rate of traffic flow rates and truck percentages. Third, employing these predicted traffic parameters, a curve-fitting modeling technique was implemented to quantify the impact of what-if lane closures on the overall traffic flow. The robustness of the proposed curve-fitting models was then scientifically verified and validated by measuring forecast accuracy. The results of this study convey the fact that MICUZ would recognize how stereotypical regional traffic patterns react to existing CWZs and lane closure tactics, and quantify the probable but reliable travel time delays at CWZs in heavily trafficked urban cores. The proposed framework provides a rigorous theoretical basis for comparatively analyzing what-if construction scenarios, enabling engineers and planners to choose the most efficient transportation management plans much more quickly and accurately

A Sensor Network System for Monitoring Short-Term Construction Work Zones

Safety hazards encountered near construction work zones are high, both in number and in the kind. There is a need to monitor traffic in such construction zones in order to improve driver and vehicle safetyIn the past traffic monitoring systems were built with high cost equipment such as inductive plates, video cameras etc. These solutions are too cost{prohibitive and invasive to be used in the large. Wireless sensor networks provide an opportunity space that can be used to address this problem. This thesis specifically targets temporary or short-term construction work zones. We present the design and implementation of a sensor network system targeted at monitoring the flow of traffic through these temporary construction work zones. As opposed to long-term work zones which are common on highways, short-term or temporary work zones remain active for a few hours or a few days at most. As such, instrumenting temporary work zones with monitoring equipment similar to those used in long-term work zones is not practical. Yet, these temporary work zones present an important problem in terms of crashes occurring in and around them. The design for this sensornet-based system for monitoring traffic is (a) inexpensive, (b) rapidly deployable, (c) requires minimal maintenance and (d) non-invasive. In this thesis we present our experiences in building this system, and testing this system in live work zones in the Greater Cleveland are

A Sensor Network System for Monitoring Short-Term Construction Work Zones

Safety hazards encountered near construction work zones are high, both in number and in the kind. There is a need to monitor traffic in such construction zones in order to improve driver and vehicle safetyIn the past traffic monitoring systems were built with high cost equipment such as inductive plates, video cameras etc. These solutions are too cost{prohibitive and invasive to be used in the large. Wireless sensor networks provide an opportunity space that can be used to address this problem. This thesis specifically targets temporary or short-term construction work zones. We present the design and implementation of a sensor network system targeted at monitoring the flow of traffic through these temporary construction work zones. As opposed to long-term work zones which are common on highways, short-term or temporary work zones remain active for a few hours or a few days at most. As such, instrumenting temporary work zones with monitoring equipment similar to those used in long-term work zones is not practical. Yet, these temporary work zones present an important problem in terms of crashes occurring in and around them. The design for this sensornet-based system for monitoring traffic is (a) inexpensive, (b) rapidly deployable, (c) requires minimal maintenance and (d) non-invasive. In this thesis we present our experiences in building this system, and testing this system in live work zones in the Greater Cleveland are

A Sensor Network System for Monitoring Short-Term Construction Work Zones

Safety hazards encountered near construction work zones are high, both in number and in the kind. There is a need to monitor traffic in such construction zones in order to improve driver and vehicle safetyIn the past traffic monitoring systems were built with high cost equipment such as inductive plates, video cameras etc. These solutions are too cost{prohibitive and invasive to be used in the large. Wireless sensor networks provide an opportunity space that can be used to address this problem. This thesis specifically targets temporary or short-term construction work zones. We present the design and implementation of a sensor network system targeted at monitoring the flow of traffic through these temporary construction work zones. As opposed to long-term work zones which are common on highways, short-term or temporary work zones remain active for a few hours or a few days at most. As such, instrumenting temporary work zones with monitoring equipment similar to those used in long-term work zones is not practical. Yet, these temporary work zones present an important problem in terms of crashes occurring in and around them. The design for this sensornet-based system for monitoring traffic is (a) inexpensive, (b) rapidly deployable, (c) requires minimal maintenance and (d) non-invasive. In this thesis we present our experiences in building this system, and testing this system in live work zones in the Greater Cleveland are

Data-Driven Multi-step Demand Prediction for Ride-Hailing Services Using Convolutional Neural Network

Ride-hailing services are growing rapidly and becoming one of the most disruptive technologies in the transportation realm. Accurate prediction of ride-hailing trip demand not only enables cities to better understand people's activity patterns, but also helps ride-hailing companies and drivers make informed decisions to reduce deadheading vehicle miles traveled, traffic congestion, and energy consumption. In this study, a convolutional neural network (CNN)-based deep learning model is proposed for multi-step ride-hailing demand prediction using the trip request data in Chengdu, China, offered by DiDi Chuxing. The CNN model is capable of accurately predicting the ride-hailing pick-up demand at each 1-km by 1-km zone in the city of Chengdu for every 10 minutes. Compared with another deep learning model based on long short-term memory, the CNN model is 30% faster for the training and predicting process. The proposed model can also be easily extended to make multi-step predictions, which would benefit the on-demand shared autonomous vehicles applications and fleet operators in terms of supply-demand rebalancing. The prediction error attenuation analysis shows that the accuracy stays acceptable as the model predicts more steps

A WSN-Based intrusion alarm system to improve safety in road work zones

Road traffic accidents are one of the main causes of death and disability worldwide. Workers responsible for maintaining and repairing roadways are especially prone to suffer these events, given their exceptional exposure to traffic. Since these actuations usually coexist with regular traffic, an errant driver can easily intrude the work area and provoke a collision. Some authors have proposed mechanisms aimed at detecting breaches in the work zone perimeter and alerting workers, which are collectively called intrusion alarm systems. However, they have several limitations and have not yet fulfilled the necessities of these scenarios. In this paper, we propose a new intrusion alarm system based on a Wireless Sensor Network (WSN). Our system is comprised of two main elements: vehicle detectors that form a virtual barrier and detect perimeter breaches by means of an ultrasonic beam and individual warning devices that transmit alerts to the workers. All these elements have a wireless communication interface and form a network that covers the whole work area. This network is in charge of transmitting and routing the alarms and coordinates the behavior of the system. We have tested our solution under real conditions with satisfactory results

Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology

INE/AUTC 10.0

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%