Sakib Mahmud Khan

Integration of Structural Health Monitoring and Intelligent Transportation Systems for Bridge Condition Assessment: Current Status and Future Direction

Recent catastrophic bridge failures clearly indicate the urgent need for improving interval-based bridge inspection procedures that are qualitative and subjective in nature. Structural Health Monitoring (SHM) can mitigate the deficiencies of interval-based inspection techniques and provide real-time diagnostic information regarding the bridge structural health. SHM is not flawless however; the variability in the vehicle characteristics and traffic operational conditions makes it prone to false diagnosis. Recent advancements in the integration of SHM with intelligent transportation systems (ITS) demonstrate the successful use of ITS devices (e.g., traffic cameras, traffic detectors) in the analysis of bridge responses to multimodal traffic with varying loads or during the critical events that cause excess vibration beyond the normal limit. In an ITS-informed SHM system, the ITS device collected data can be integrated with SHM to increase the reliability and accuracy of the SHM system. This integration would reduce the possibility of false diagnosis of damages detected by the SHM system (e.g., vibrations caused by heavy vehicles on a bridge could be read by a SHM sensor as a structural health problem of the bridge), which would eventually decrease the bridge maintenance costs. Similarly, in SHM-informed ITS system, SHM sensors can provide data on bridge health condition for ITS applications, where ITS uses this bridge health condition information for real-time traffic management. In this paper, literature related to both ITS-informed SHM and SHM-informed ITS is reviewed. Based on the literature review, potential challenges and future research directions associated with ITS-SHM integration are also discussed.

Real-Time Traffic State Estimation With Connected Vehicles

A novel framework is developed in this paper, to increase the real-time roadway traffic condition assessment accuracy, which integrates connected vehicle technology (CVT) with artificial intelligence (AI) paradigm forming a CVT-AI method. Traffic density is a major indicator of traffic conditions. In this paper, the traffic operational condition is assessed based on traffic density. A simulated network of Interstate 26 in South Carolina is developed to investigate the effectiveness of the method. The assumption is that the vehicle onboard units will forward the CV generated data to the edge devices (e.g., roadside units) for further processing. CV generated distance headway and number of stops, and speed data are used to estimate traffic density. This paper reveals that, with 20% and greater CV penetration levels, the accuracy of the density information with the AI-aided CVT is a minimum of 85%. Moreover, this paper demonstrates that the integrated CVT-AI method yields a higher accuracy with the increase of CV penetration levels. Level of service (LOS) is the indicator of traffic congestion level on highways and is described with traffic density in terms of passenger car/mile/lane for a specific free flow speed. LOS estimated using the CVT-AI density estimation method is compared with the density estimation algorithm used by the Caltrans Performance Measurement System (PeMS), which relies on the occupancy and flow data collected by the freeway inductive loop detectors. With a 10% or more CV penetration, higher accuracy is achieved using the CVT-AI algorithm compared with the PeMS density estimation algorithm.

ITS for One of the Most Congested Cities in the Developing World?Dhaka Bangladesh: Challenges and Potentials [ITS in Developing Countries]

Transportation planners from Dhaka city always confront the paradox of adjusting an ever increasing travel demand with the limited available resources. Thus, Dhaka city motorists suffocate while they idle in the intolerable gridlock for hours. ITS applications can play an active role in mitigating this gridlock by dramatically changing the existing problems of limited roadway capacity, vulnerable safety and neglected public transportation system. However, after carefully scrutinizing the prevailing traffic conditions and their interlinked problems, the authors have identified various potential ITS applications despite limited active ITS applications in Dhaka city. Observations indicate that rather than perpetuating the concept of ITS in Dhaka city as mythological, it is indeed a cost-effective method for both the users and the government for traffic management.

Lessons Learned from the Real-World Deployment of a Connected Vehicle Testbed

The connected vehicle (CV) system promises unprecedented safety, mobility, environmental, economic, and social benefits, which can be unlocked using the enormous amount of data shared between vehicles and infrastructure (e.g., traffic signals, centers). Real-world CV deployments, including pilot deployments, help solve technical issues and observe potential benefits, both of which support the broader adoption of the CV system. This study focused on the Clemson University Connected Vehicle Testbed (CU-CVT) with the goal of sharing the lessons learned from the CU-CVT deployment. The motivation of this study was to enhance early CV deployments with the objective of depicting the lessons learned from the CU-CVT testbed, which includes unique features to support multiple CV applications running simultaneously. The lessons learned in the CU-CVT testbed are described at three different levels: (i) the development of system architecture and prototyping in a controlled environment, (ii) the deployment of the CU-CVT testbed, and (iii) the validation of the CV application experiments in the CU-CVT. Field experiments with CV applications validated the functionalities needed for running multiple diverse CV applications simultaneously using heterogeneous wireless networking, and meeting real-time and non-real-time application requirements. The unique deployment experiences, related to heterogeneous wireless networks, real-time data aggregation, data dissemination and processing using a broker system, and data archiving with big data management tools, gained from the CU-CVT testbed, could be used to advance CV research and guide public and private agencies for the deployment of CVs in the real world.

Current Practice of Design and Delivery of Online Training for Transportation Professionals at Public Agencies

To improve the effectiveness of transportation professionals in their respective jobs and successfully meet changing capability requirements, public agencies often offer online training. This article presents the current practices of design criteria and delivery method of such training through a three-faceted approach: the review of published materials, an online survey of transportation agency professionals, and follow-up telephone interviews. This study revealed that some of the most important considerations of successful online training programs are (a) the inclusion of interactive components within the training modules to keep participants engaged, (b) a short duration for each of the training modules to retain participants’ attentiveness, and (c) the provision of quizzes to assess participants’ understanding of the material.

Social Media Data in Transportation

The Internet and the mobile phone continue to proliferate worldwide. As they become indispensable communication tools for people around the world, the usage of social media applications, like Twitter, INSTAGRAM, and Facebook, is becoming more popular every day. As social media includes postings about people’s daily activities, including travel, it can potentially be a rich source of data for supporting transportation planning and operations. Looking forward into the future, an abundance of new sensor devices such as in-vehicle sensors or handheld mobile devices will provide new sets of real-time data (e.g., on traveler locations, speeds, and routes), which could be integrated with social media data to further improve understanding of users’ behaviors and traffic conditions in real time. However, to take advantage of these data for transportation applications, we need to understand the characteristics of the data generated by a wide range of social media platforms and the methods that can be used to mine, organize, store, process, interpret, and communicate these data. Moreover, we must do so while ensuring the social media user privacy and the data security. This chapter discusses social media data characteristics that are relevant to transportation-related applications, and issues involving their use and analysis.

Characteristics of Intelligent Transportation Systems and Its Relationship With Data Analytics

Abstract Transportation continues to play a strategic role in our worldwide economy, delivering goods and people through increasingly complex, interconnected, and multimodel transportation systems. Unfortunately, the complexities of modern transportation cannot be managed using yesterday’s tools. For example, the data collected by the technologies of the intelligent transportation systems (ITS) are increasingly complex and are characterized by heterogeneous formats, large volume, nuances in spatial and temporal processes, and frequent real-time processing requirements. Simple data processing, integration, and analytics tools do not meet the needs of complex ITS data processing tasks. The application of emerging data analytic systems and methods, with effective data collection and information distribution systems, provides opportunities which are required for building the ITSs of today and tomorrow. Imagine a world in which all products always arrive in a predetermined order, on time, at low cost, with consistent results and are produced by a happy and productive transportation workforce. People travel in safe, comfortable, efficient systems that are affordable, convenient, and friendly to our environment. An educated transportation system workforce, including engineers, scientists, and operational professionals, has the tools to design, build, test, provision, operate and optimize the systems. It also has the knowledge to use these tools. This educated workforce is inherently multidisciplinary combining expertise from transportation engineering, software engineering, computer science, business, statistics, and mathematics. ITS turn data into actionable knowledge enabling transportation users to make informed decisions ensuring the safe and efficient use of the facilities. For example, in such a system, every traveler has access to the most reliable and up-to-date status of almost all transportation modes from any point on the transportation network. Travelers use devices that include instrumented vehicles, smartphones, tablet computer, and roadside information displays. They can then choose the mode and route that will give them the minimum travel time and distance making dynamic adjustments from real-time information. In this chapter, we will demonstrate that ITS is data-intensive application. First, we provide a summary of the sources and characteristics of ITS data, discussing the relationship of ITS to data analytics. Later, a review of the US National ITS architecture is given as an example framework for ITS planning, design, and deployment, with an overview of ITS applications and their relationships to data analytics. Finally, a brief history of ITS deployment around the world is given, and a future characterized by the technological advances in ITS is presented.