Rabi G. Mishalani

Network state estimation and prediction for real-time traffic management

Advanced Traveler Information Systems (ATIS) and Advanced Traffic Management Systems (ATMS) have the potential to contribute to the solution of the traffic congestion problem. DynaMIT is a real-time system that can be used to generate guidance for travelers. The main principle on which DynaMIT is based is that information should be consistent, and user optimal. Consistency implies that the traffic conditions experienced by the travelers are consistent with the condition assumed in generating the guidance. To generate consistent user optimal information, DynaMIT performs two main functions: state estimation and prediction. A demand simulator and a supply simulator interact to perform these tasks. A case study demonstrates the value of the system.

Real-time simulation of traffic demand-supply interactions within DynaMIT

DynaMIT is a simulation-based real-time system designed to estimate the current state of a transportation network, predict future traffic conditions, and provide consistent and unbiased information to travelers. To perform these tasks, efficient simulators have been designed to explicitly capture the interactions between transportation demand and supply. The demand reflects both the OD flow patterns and the combination of all the individual decisions of travelers while the supply reflects the transportation network in terms of infrastructure, traffic flow and traffic control. This paper describes the design and specification of these simulators, and discusses their interactions.

Passenger Wait Time Perceptions at Bus Stops: Empirical Results and Impact on Evaluating Real-Time Bus Arrival Information

This study quantifies the relationship between the perceived and actual waiting times experienced by passengers at a bus stop. Understanding such a relationship would be useful in quantifying the value of providing real-time information to passengers on the time until the next bus is expected to arrive at a bus stop. Data on perceived and actual passenger waiting times, along with socioeconomic characteristics, were collected at bus stops where no real-time bus arrival information is provided, and relationships between perceived and actual waiting times are estimated. The results indicate that passengers do perceive time to be greater than the actual amount of time waited. However, the hypothesis that the rate of change of perceived time does not vary with respect to the actual waiting time could not be rejected (over a range of 3 to 15 minutes). Assuming that a passenger’s perceived waiting time is equal to the actual time when presented with accurate real-time bus arrival information, the value of the eliminated additional time is assessed in the form of reduced vehicle hours per day resulting from a longer headway that produces the same mean passenger waiting time. The eliminated additional time is also assessed in the form of uncertainty in the headway resulting in the same extra waiting time. Naturally, such benefits of passenger information can only be confirmed when the actual effect of information on the perception of waiting time is quantified.

Real-Time Ridesharing: Opportunities and Challenges in Using Mobile Phone Technology to Improve Rideshare Services

In recent years, an innovative ridesharing service relying heavily on advanced mobile phone technologies known as real-time ridesharing or dynamic ridesharing, has gained popularity in some groups: providers, organizations, and employers. Traditionally, rideshare arrangements between two or more unrelated individuals for commuting purposes have been relatively inflexible, long-term arrangements. Real-time ridesharing attempts to add flexibility to rideshare arrangements by allowing drivers and passengers to arrange occasional shared rides ahead of time or on short notice. The addition of this service innovation presents opportunities to overcome existing rideshare challenges but also leads to new challenges. The overall goal of this study was to provide a foundation for further realtime ridesharing research. The aims of the study were to identify, highlight, and discuss the potential benefits of and obstacles to real-time ridesharing and to point to the next steps to understand better and possibly advance this mode of travel. A definition of real-time ridesharing was given, followed by a comprehensive categorization of challenges hindering greater rideshare participation. The information gathered suggested that rather than being a single challenge to be overcome, the rideshare challenge was a series of economic, behavioral, institutional, and technological obstacles to be addressed. Potential opportunities and obstacles created by real-time innovations were then highlighted. Several recommendations are provided toward next steps to understand further how rideshare participants use real-time services, focusing on the need for multiple, comprehensive trials of real-time rideshare.

Service Reliability Measurement Using Automated Fare Card Data: Application to the London Underground

This paper explores the potential of using automated fare card data to quantify the reliability of service as experienced by passengers of rail transit systems. The distribution of individual passenger journey times can be accurately estimated for those systems requiring both entry and exit fare card validation. With the use of this information, a set of service reliability measures is developed that can be used to routinely monitor performance, gain insights into the causes of unreliability, and serve as an input into the evaluation of transit service. An estimation methodology is proposed that classifies performance into typical and nonrecurring conditions, which allows analysts to estimate the level of unreliability attributable to incidents. The proposed measures are used to characterize the reliability of one line in the London Underground under typical and incident-affected conditions with the use of data from the Oyster smartcard system for the morning peak period. A validation of the methodology with the use of incident-log data confirms that a large proportion of the unreliability experienced by passengers can be attributed to incident-related disruptions. In addition, the study revealed that the perceived reliability component of the typical Underground trip exceeds its platform wait time component and equals about half of its on-train travel time as well as its station access and egress time components, suggesting that sizable improvements in overall service quality can be attained through reliability improvements.

Impact of Lane-Change Maneuvers on Congested Freeway Segment Delays: Pilot Study

Freeway traffic congestion and associated delays have become a serious problem throughout much of the world. Among factors studied in relation to this congestion, lane-change maneuvers (LCMs) are suspected to be one source of traffic delay. Little empirical research has been done on this topic despite how common the lane-changing process is on multilane roadway segments. Two main problems hinder research: first, it is difficult to distinguish changes in delay caused by LCMs from that of a preexisting delay caused by a queue; second, quantifying LCMs is difficult because it requires both spatial and temporal coverage. This paper presents the concept of delay caused by LCMs and proposes a method to estimate such delays within a given lane relative to the situation in which no LCMs have taken place. This estimation method makes it possible to investigate the impact of LCMs on traffic delays based on vehicle trajectory data, which record the time and location of each vehicle at each instant. This preliminary study shows the effectiveness of the proposed method to estimate delays caused by LCMs and reveals how LCMs affect delays on congested freeway segments.

MODELING THE SPATIAL BEHAVIOR OF INFRASTRUCTURE CONDITION

A new understanding of the spatial behavior of infrastructure condition is presented and a methodology for identifying this behavior is developed. Based on two deterioration mechanisms, causal and interactive, a nonstationary stochastic spatial model with a piecewise constant mean function is proposed. Based on this model, a methodology founded on nonparametric cluster analysis and dynamic programming is developed to identify the optimal spatial regions, referred to as fields, within which behavior is uniform and, consequently, condition can be estimated accurately. Validation using detailed distress data from three roadway facilities, each 15 km long, is presented. The value of the model is also demonstrated by comparing it against existing methods for characterizing infrastructure condition over space.

Iterative Proportional Fitting Procedure to Determine Bus Route Passenger Origin-Destination Flows

The advent of automatic passenger counter (APC) technologies is resulting in the collection of comprehensive boarding and alighting data on an ongoing basis across transit networks. The availability of APC data offers a new opportunity to determine origin–destination (O-D) flows on a frequent and comprehensive basis. In this paper, the performance of a simple procedure for route-level O-D flow determination requiring only boarding and alighting data is investigated. Specifically, the performance of the iterative proportional fitting (IPF) procedure used with a null base matrix is examined on the basis of a field experiment in which true O-D flows are observed. Because of the noninformative nature of the null matrix, using the IPF procedure with the null matrix as its input base may not be expected to produce good results. In a comparison of empirical results with those produced by other benchmark procedures, the IPF–null procedure is found to perform surprisingly well. The quality of the resulting matrices appears to be roughly similar to that of matrices derived from an onboard survey, the benchmark for what has been achieved in practice, but at much higher cost. The results indicate that much can be gained from using readily available APC data, even when the simple IPF–null procedure is applied. Moreover, using the better base obtained from an onboard survey with the IPF procedure improved performance, but less markedly compared with use of the null base; this difference indicates that combining onboard survey information with APC data provides a better O-D matrix than what can be derived from an onboard survey alone, even when the simple IPF procedure is used.

Decision Factors in Service Control on High-Frequency Metro Line: Importance in Service Delivery

Service control—the task of implementing the timetable in daily operations on a metro line—plays a key role in service delivery, because it influences the quality of the service provided to passengers. Shortfalls of previous research on the role and importance of service control have been noted. A framework intended to remedy some of these shortfalls is proposed. An important element of this framework is the description of the full decision environment in which service control takes place. On the basis of insights gained from extended visits to a control center, the reliability of the system is found to depend on many endogenous factors. These factors were not previously recognized in a comprehensive manner by either researchers or practitioners. Aside from the objectives of maintaining adequate levels of service from an operations perspective and minimizing the impact of schedule deviations on passengers, the management of crew and rolling stock, safety, and infrastructure capacity are major considerations in service control decisions. Given the uncertain environment in which service control operates, a strong preference was observed among controllers for manageable and robust control strategies. An example is discussed in which service controllers react to two similar disruptions with different recovery strategies, mainly because of crew management considerations. This research demonstrates the importance of a comprehensive understanding of the objectives and constraints faced by service controllers in daily operations.

Identifying Homogeneous Periods in Bus Route Origin-Destination Passenger Flow Patterns From Automatic Passenger Counter Data

Bus passenger origin-destination (O-D) flow matrices portray information on travel patterns that can be used for route planning, design, and operations functions. Because travel patterns are known to vary throughout the day, O-D flow matrices can be expected to vary throughout the day as well. A method identifies time-of-day periods of homogeneous normalized bus route O-D passenger flow matrices in which a normalized matrix depicts the probabilities that a random passenger in the homogeneous period will travel from various origin stops to various destination stops on the route. The method uses bus trip automatic passenger counter data to estimate trip-level O-D matrices, aggregates the trip-level O-D matrices into elemental matrices for relatively short time periods, and then considers these elemental matrices as inputs to a traditional clustering procedure that is modified to ensure that a cluster indicating a period of homogeneous normalized O-D flow spans a continuous time period during the day. The homogeneous O-D flow period method is applied to empirical automatic passenger counter data collected on a bus route for which temporal travel patterns are understood. The time periods identified correspond well to the a priori understanding of travel patterns. A parallel method that uses passenger volume, rather than estimated normalized O-D flow matrices, is applied to the same data. The periods identified by this volume-based approach are not responsive to the changes in the normalized O-D flow patterns determined by the homogeneous O-D flow period identification method.