Evaluating on-street parking policy

This paper uses a formal model to examine the welfare gains from a marginal increase in the price of on-street parking. The benefits of such a policy are shown to depend on the improvement in search externalities in the on-street parking market itself, plus effects on other distorted urban transport markets, including congested freeway and backroad use, mass-transit and off-street parking. The paper makes two further contributions. The model is sufficiently general that several well-known results from the parking literature emerge as special cases. The model is used to review the existing literature and highlights findings in separate parts of literature. Finally, a numerical simulation model is used to investigate the order of magnitude of an optimal urban parking fee. In particular, these results confirm the importance of taking into accounts effects on other distorted transport markets when deciding upon the level of the price for on-street parking. The model confirms that while parking pricing reform may lead to substantial improvements in parking search times, there is little overall impact on road congestion levels.

Measuring traffic lane-changing by converting video into space–time still images

Empirical data is needed in order to extend our knowledge of traffic behavior. Video recordings are used to enrich typical data from loop detectors. In this context, data extraction from videos becomes a challenging task. Setting automatic video processing systems is costly, complex, and the accuracy achieved is usually not enough to improve traffic flow models. In contrast “visual” data extraction by watching the recordings requires extensive human intervention. A semiautomatic video processing methodology to count lane-changing in freeways is proposed. The method allows counting lane changes faster than with the visual procedure without falling into the complexities and errors of full automation. The method is based on converting the video into a set of space–time still images, from where to visually count. This methodology has been tested at several freeway locations near Barcelona (Spain) with good results. A user-friendly implementation of the method is available on http://bit.ly/2yUi08M.Peer ReviewedPostprint (published version

Artificial intelligence enabled automatic traffic monitoring system

The rapid advancement in the field of machine learning and high-performance computing have highly augmented the scope of video-based traffic monitoring systems. In this study, an automatic traffic monitoring system is proposed that deploys several state-of-the-art deep learning algorithms based on the nature of traffic operation. Taking advantage of a large database of annotated video surveillance data, deep learning-based models are trained to track congestion, detect traffic anomalies and tabulate vehicle counts. To monitor traffic queues, this study implements a Mask region-based convolutional neural network (Mask R-CNN) that predicts congestion using pixel-level segmentation masks on classified regions of interest. Similarly, the model was used to accurately extract traffic queue-related information from infrastructure mounted video cameras. The use of infrastructure-mounted CCTV cameras for traffic anomaly detection and verification is further explored. Initially, a convolutional neural network model based on you only look once (YOLO), a popular deep learning framework for object detection and classification is deployed. The following identification model, together with a multi-object tracking system (based on intersection over union -- IOU) is used to search for and scrutinize various traffic scenes for possible anomalies. Likewise, several experiments were conducted to fine-tune the system's robustness in different environmental and traffic conditions. Some of the techniques such as bounding box suppression and adaptive thresholding were used to reduce false alarm rates and refine the robustness of the methodology developed. At each stage of our developments, a comparative analysis is conducted to evaluate the strengths and limitations of the proposed approach. Likewise, IOU tracker coupled with YOLO was used to automatically count the number of vehicles whose accuracy was later compared with a manual counting technique from CCTV video feeds. Overall, the proposed system is evaluated based on F1 and S3 performance metrics. The outcome of this study could be seamlessly integrated into traffic system such as smart traffic surveillance system, traffic volume estimation system, smart work zone management systems, etc.by Vishal MandalIncludes bibliographical reference

Using microscopic video data measures for driver behavior analysis during adverse winter weather: opportunities and challenges

ABSTRACT: This paper presents a driver behavior analysis using microscopic video data measures including vehicle speed, lane-changing ratio, and time to collision. An analytical framework was developed to evaluate the effect of adverse winter weather conditions on highway driving behavior based on automated (computer) and manual methods. The research was conducted through two case studies. The first case study was conducted to evaluate the feasibility of applying an automated approach to extracting driver behavior data based on 15 video recordings obtained in the winter 2013 at three different locations on the Don Valley Parkway in Toronto, Canada. A comparison was made between the automated approach and manual approach, and issues in collecting data using the automated approach under winter conditions were identified. The second case study was based on high quality data collected in the winter 2014, at a location on Highway 25 in Montreal, Canada. The results demonstrate the effectiveness of the automated analytical framework in analyzing driver behavior, as well as evaluating the impact of adverse winter weather conditions on driver behavior. This approach could be applied to evaluate winter maintenance strategies and crash risk on highways during adverse winter weather conditions

Issues Related to the Emergence of the Information Superhighway and California Societal Changes, IISTPS Report 96-4

The Norman Y. Mineta International Institute for Surface Transportation Policy Studies (IISTPS) at San José State University (SJSU) conducted this project to review the continuing development of the Internet and the Information Superhighway. Emphasis was placed on an examination of the impact on commuting and working patterns in California, and an analysis of how public transportation agencies, including Caltrans, might take advantage of the new communications technologies. The document reviews the technology underlying the current Internet “structure” and examines anticipated developments. It is important to note that much of the research for this limited-scope project was conducted during 1995, and the topic is so rapidly evolving that some information is almost automatically “dated.” The report also examines how transportation agencies are basically similar in structure and function to other business entities, and how they can continue to utilize the emerging technologies to improve internal and external communications. As part of a detailed discussion of specific transportation agency functions, it is noted that the concept of a “Roundtable Forum,” growing out of developments in Concurrent Engineering, can provide an opportunity for representatives from multiple jurisdictions to utilize the Internet for more coordinated decision-making. The report also included an extensive analysis of demographic trends in California in recent years, such as commute and recreational activities, and identifies how the emerging technologies may impact future changes

Real-time crash prediction models: State-of-the-art, design pathways and ubiquitous requirements

Proactive traffic safety management systems can monitor traffic conditions in real-time, identify the formation of unsafe traffic dynamics, and implement suitable interventions to bring unsafe conditions back to normal traffic situations. Recent advancements in artificial intelligence, sensor fusion and algorithms have brought about the introduction of a proactive safety management system closer to reality. The basic prerequisite for developing such a system is to have a reliable crash prediction model that takes real-time traffic data as input and evaluates their association with crash risk. Since the early 21st century, several studies have focused on developing such models. Although the idea has considerably matured over time, the endeavours have been quite discrete and fragmented at best because the fundamental aspects of the overall modelling approach substantially vary. Therefore, a number of transitional challenges have to be identified and subsequently addressed before a ubiquitous proactive safety management system can be formulated, designed and implemented in real-world scenarios. This manuscript conducts a comprehensive review of existing real-time crash prediction models with the aim of illustrating the state-of-the-art and systematically synthesizing the thoughts presented in existing studies in order to facilitate its translation from an idea into a ready to use technology. Towards that journey, it conducts a systematic review by applying various text mining methods and topic modelling. Based on the findings, this paper ascertains the development pathways followed in various studies, formulates the ubiquitous design requirements of such models from existing studies and knowledge of similar systems. Finally, this study evaluates the universality and design compatibility of existing models. This paper is, therefore, expected to serve as a one stop knowledge source for facilitating a faster transition from the idea of real-time crash prediction models to a real-world operational proactive traffic safety management system

Intelligent Transportation Systems (ITS): A Survey Of What It Is, What It Does, Where It Faulters, And Where To Go With It

The world of technology continues to find itself incorporated into an ever-expanding number of fields with a rapidly increasing number of applications. One of these is transportation, under the umbrella of Intelligent Transportation Systems (ITS). The intention of this application at a macro scale is to increase the surface transport safety, efficiency, and convenience. As technological improvements continue to be made, ITS grows in popularity and implementation, and is now found in many cities across the United States. Correct implementation of ITS could have huge benefits in the transportation sector, but without thinking about its implications now, there is a risk of worsening already existing issues. Much of the information regarding ITS is scattered through various research publications making it difficult to understand what it is and what effects it has on the places it is implemented in. The purpose of this paper is to provide an easily accessible reference document that gives a general overview of these factors, allowing decision makers to gain a quick understanding of the topic and thus make better informed choices. Some directions for further research are also given to illustrate what is currently unknown about ITS and where potential improvements could be made