Route Guidance Algorithms Effective for All Levels of Take-Up and Congestion.

This paper describes work carried out under the EC `DRIVE' programme, the aim being to develop route guidance strategies which direct users to multiple routes between each origin-destination pair, and thereby provide stable and effective guidance even when a large proportion of drivers are guided. A model is proposed in which guided and unguided drivers have different route choice assumptions, but are still able to interact with one another; the guidance may be based on either user or system objectives. Conditions are deduced under which the resulting route pattern is guaranteed to exist and be stable. To assess the performance of the strategies, simulations are carried out on two real-life networks, for a number of different demand levels, levels of equipped vehicles, levels of error in (or adherence to) the guidance recommendations, and different guidance criteria. The simulations are extended, in order to examine firstly the influence of behaviour of unguided drivers on the benefits obtained, and secondly the performance of the strategies in cases of unforeseen variations in network conditions. Finally, some comparisons are drawn with a route guidance strategy developed in a parallel `DRIVE' project, where only one route is recommended per origin-destination pair

Data Support of Advanced Traveler Information System Considering Connected Vehicle Technology

Traveler information systems play a significant role in most travelers’ daily trips. These systems assist travelers in choosing the best routes to reach their destinations and possibly select suitable departure times and modes for their trips. Connected Vehicle (CV) technologies are now in the pilot program stage. Vehicle-to-Infrastructure (V2I) communications will be an important source of data for traffic agencies. If this data is processed properly, then agencies will be able to better determine traffic conditions, allowing them to take proper countermeasures to remedy transportation system problems under different conditions. This research focuses on developing methods to assess the potential of utilizing CV data to support the traveler information system data collection process. The results from the assessment can be used to establish a timeline indicating when an agency can stop investing, at least partially, in traditional technologies, and instead rely on CV technologies for traveler information system support. This research utilizes real-world vehicle trajectory data collected under the Next Generation Simulation (NGSIM) program and simulation modeling to emulate the use of connected vehicle data to support the traveler information system. NGSIM datasets collected from an arterial segment and a freeway segment are used in this research. Microscopic simulation modeling is also used to generate required trajectory data, allowing further analysis, which is not possible using NGSIM data. The first step is to predict the market penetration of connected vehicles in future years. This estimated market penetration is then used for the evaluation of the effectiveness of CV-based data for travel time and volume estimation, which are two important inputs for the traveler information system. The travel times are estimated at different market penetrations of CV. The quality of the estimation is assessed by investigating the accuracy and reliability with different CV deployment scenarios. The quality of volume estimates is also assessed using the same data with different future scenarios of CV deployment and partial or no detector data. Such assessment supports the identification of a timeline indicating when CV data can be used to support the traveler information system

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

Analysis of dynamic traffic control and management strategies

Ph.DDOCTOR OF PHILOSOPH

The provision of real-time information for passengers in metro networks Case studies: London and Hong Kong

This study looks at discovering information about the dynamics of a metro network, in real-time, using entry and exit data from the passengers’ smart cards. The data shows to be a valuable source of information about the current conditions of the network for both operators and passengers. An algorithm was developed which used real-time data to determine journey time characteristics, and to determine deviations from normal travel time and the extent to which these constitute a delay. This study focuses on the London Underground network and the Hong Kong MTR network as case studies to test the algorithm using the data produced by the automated ticketing systems. It aims to mine the data to provide information that can be used by passengers of the network. This information can lead to passengers knowing optimal routes, a realistic travel time and the number of minutes a delay may cost them; when the delay may be caused by congestion or service problems. Operationally this can allow for delay status reports to be more realistic, dynamic and responsive to crowding and provide information to the operators about the dynamics of the network in real time