Intelligent Transportation Systems Strategic Plan (Phase I Report)

This interim report on an Intelligent Transportation Systems Strategic (ITS) Plan has been developed as documentation of the process of offering a vision for ITS and recommending an outline for organizational structure, infrastructure, and long-term planning for ITS in Kentucky. This plan provides an overview of the broad scope of ITS and relationships between various Intelligent Vehicle Highway Systems (IVHS) functional areas and ITS user service areas. Three of the functional areas of ITS have been addressed in this interim report with sections devoted to mission, vision, goals, and potential technology applications. Within each of the three areas, recommendations have been made for applications and technologies for deployment. A more formalized business plan for will be developed to recommend specific projects for implementation. Those three functional areas are 1) Advanced Rural Transportation Systems (ARTS), 2) Advanced Traveler Information Systems (ATIS), and 3) Commercial Vehicle Operations (CVO). A survey of other states was conducted to determine the status of the development of ITS strategic plans. Information received from the 11 states that had completed strategic plans was used to determine the overall approach taken in development of the plans and to evaluate the essential contents of the reports for application in Kentucky. Kentucky\u27s ITS Strategic Plan evolved from an early decision by representatives of the Kentucky Transportation Cabinet (KyTC) to formalize the procedure by requesting the Kentucky Transportation Center to prepare a work plan outlining the proposed tasks. Following several introductory meetings of the Study Advisory Committee, additional focus group meetings were held with various transportation representatives to identify ITS issues of importance. Results from these meetings were compiled and used as input to the planning process for development of the Strategic Plan components of ARTS and ATIS. The development of a strategic plan for Commercial Vehicle Operations originated from a different procedure than did the other functional areas of ITS. As part of well-developed commercial vehicle activities through the ITS-related programs of Advantage I-75 and CVISN, Kentucky has become a national leader in this area and has developed a strategic plan of advanced technology applications to commercial vehicles. The strategic plan for Commercial Vehicle Operations was developed out of the convergence of several parallel processes in Kentucky. Empower Kentucky work teams had met over a two-year period to develop improved and more efficient processes for CVO in Kentucky. Their conclusions and recommendations encouraged the further activities of the Kentucky ITS/CVO working group that first convened in the summer of 1996. In an effort to conceptually organize the various ITS/CVO activities in Kentucky, and as a commitment to the CVISN Mainstreaming plan, an inclusive visioning exercise was held in early 1997. Out of this exercise emerged the six critical vision elements that guided the CVO strategic plan. The remaining functional areas to be included in the ITS Strategic Plan will be addressed in the second phase of this study. Those areas are Advanced Traffic Management Systems (ATMS), Advanced Vehicle Control Systems (AVCS), and Advanced Public Transportation Systems (APTS). It is anticipated that a process similar to that developed for the first phase of this study will continue

Automated Transit Networks (ATN): A Review of the State of the Industry and Prospects for the Future, MTI Report 12-31

The concept of Automated Transit Networks (ATN) - in which fully automated vehicles on exclusive, grade-separated guideways provide on-demand, primarily non-stop, origin-to-destination service over an area network – has been around since the 1950s. However, only a few systems are in current operation around the world. ATN does not appear “on the radar” of urban planners, transit professionals, or policy makers when it comes to designing solutions for current transit problems in urban areas. This study explains ATN technology, setting it in the larger context of Automated Guideway Transit (AGT); looks at the current status of ATN suppliers, the status of the ATN industry, and the prospects of a U.S.-based ATN industry; summarizes and organizes proceedings from the seven Podcar City conferences that have been held since 2006; documents the U.S./Sweden Memorandum of Understanding on Sustainable Transport; discusses how ATN could expand the coverage of existing transit systems; explains the opportunities and challenges in planning and funding ATN systems and approaches for procuring ATN systems; and concludes with a summary of the existing challenges and opportunities for ATN technology. The study is intended to be an informative tool for planners, urban designers, and those involved in public policy, especially for urban transit, to provide a reference for history and background on ATN, and to use for policy development and research

Sustainable Passenger Transportation: Dynamic Ride-Sharing

Ride-share systems, which aim to bring together travelers with similar itineraries and time schedules, may provide significant societal and environmental benefits by reducing the number of cars used for personal travel and improving the utilization of available seat capacity. Effective and efficient optimization technology that matches drivers and riders in real-time is one of the necessary components for a successful ride-share system. We formally define dynamic ride-sharing and outline the optimization challenges that arise when developing technology to support ride-sharing. We hope that this paper will encourage more research by the transportation science and logistics community in this exciting, emerging area of public transportation

The data chase : what's out there on trade costs and nontariff barriers ?

Trade costs and nontariff barriers are at the forefront of discussions on competitiveness and expanding trade opportunities for developing countries. This paper provides a summary overview of data and indicators relevant to these issues and has been informed by work underway at the World Bank on trade facilitation over the past several years to catalogue data sets and indicators. Although there has been progress in expanding data sets and developing policy-relevant indicators on trade costs and barriers, much more is needed. In order to assess progress toward achieving the Millennium Development Goals, evaluating the impact of development projects, and whether meeting Aid for Trade goals will be met, for example, a dedicated and expansive new effort to collect and assess data is needed. This paper attempts to highlight gaps in data on trade costs and provides insight into the type of new data that might be developed in the future.Transport Economics Policy&Planning,Economic Theory&Research,Trade Law,Free Trade,Trade Policy

Exploring the relationship between intelligent transport system capability and business agility within the Bus Rapid Transit in South Africa

Abstract: More than 65% of South Africans use public transportation to access educational, business, and financial activity. Mobility of individuals and products, particularly in metropolitan areas, suffers from delays, unreliability, absence of safety and air pollution. On the other hand, mobility demand is increasing quicker than South Africa's accessible infrastructure. Public transport services are poor in general, but this picture is transforming a high-quality mass transit system using high-capacity buses along dedicated bus lanes by implementing the Bus Rapid Transit (BRT) system. The BRT system appeared as the leading mode of urban passenger transit in the first decade of the twenty-first century after a few pioneering applications in the later portion of the twentieth century. In addition, Intelligent Transport System’s (ITS) advantages motivate both advanced and developing nations, such as South Africa, to invest in these techniques rather than spending enormous quantities on expanding the transportation network. Various stakeholders in government, academia and industry are in the process of presenting a shared vision of this new strategy and first practical steps should be taken towards this objective. Intelligent transport system capacity can provide better and more inclusive public transportation facilities to commuters through enhanced reliability and accessibility; to operators through efficiency gains; and to customers and operators in terms of cost-effectiveness and service provision affordability. International experience shows that capacities of the ITS can boost transportation profits by as much as 10-15%...D.Phil. (Engineering Management

Modeling temporal variations in travel demand for intelligent transportation systems

The imbalance between demand and supply on transportation networks, especially during peak periods, leads to significant level of congestion. Potential solutions to alleviate congestion problems include enhancing system capacity and effective utilization of available capacity--i.e., traffic demand management. Intelligent transportation system (ITS) initiatives such as travel demand management systems (TDMS) and traveler information systems (TIS) refer to demand management as an objective. The success of these initiatives rely heavily on an ability to accurately estimate the temporal variations in travel demand in near real-time. The focus of this dissertation is on developing a methodology for estimating temporal variations in travel demand in urban areas; A significant portion of daily congestion on urban transportation networks occur during peak periods. A majority of trips during peak periods are work trips. The peak study period is divided into several time slices to facilitate simulation and modeling. A methodology is developed to estimate origin-destination (O-D) trip tables for each time slice. Trip attractions during each time slice, for each traffic analysis zone (TAZ), are estimated using pertinent characteristics of the TAZ. The O-D trip tables for each time slice are estimated as a function of trip attractions for the time slice, total trip productions during the peak period and the travel time matrix for the peak period. These O-D trip tables for each time slice and the existing network conditions can be used to assign trips in near real-time; The algorithm is coded using C++ programming language. The model is first tested on various small hypothetical cases with 5 TAZs, 10 TAZs, 15 TAZS and 20 TAZs respectively. The results obtained are as expected. The robustness of the model is tested using the hypothetical case with 10 TAZs. Since, testing and validating the model on large real world networks is important, the model is tested with 1995 data obtained for the Las Vegas valley. The results are consistent with that obtained for the hypothetical cases. The model is tested on Silicon Graphics IP 27 with IRIX version 6.4 as the operating system. For almost all the scenarios, the run time is less than 3 minutes. This strengthens the notion that the model can be implemented in real time

A Robust Integrated Multi-Strategy Bus Control System via Deep Reinforcement Learning

An efficient urban bus control system has the potential to significantly reduce travel delays and streamline the allocation of transportation resources, thereby offering enhanced and user-friendly transit services to passengers. However, bus operation efficiency can be impacted by bus bunching. This problem is notably exacerbated when the bus system operates along a signalized corridor with unpredictable travel demand. To mitigate this challenge, we introduce a multi-strategy fusion approach for the longitudinal control of connected and automated buses. The approach is driven by a physics-informed deep reinforcement learning (DRL) algorithm and takes into account a variety of traffic conditions along urban signalized corridors. Taking advantage of connected and autonomous vehicle (CAV) technology, the proposed approach can leverage real-time information regarding bus operating conditions and road traffic environment. By integrating the aforementioned information into the DRL-based bus control framework, our designed physics-informed DRL state fusion approach and reward function efficiently embed prior physics and leverage the merits of equilibrium and consensus concepts from control theory. This integration enables the framework to learn and adapt multiple control strategies to effectively manage complex traffic conditions and fluctuating passenger demands. Three control variables, i.e., dwell time at stops, speed between stations, and signal priority, are formulated to minimize travel duration and ensure bus stability with the aim of avoiding bus bunching. We present simulation results to validate the effectiveness of the proposed approach, underlining its superior performance when subjected to sensitivity analysis, specifically considering factors such as traffic volume, desired speed, and traffic signal conditions