Steven I-Jy Chien

DYNAMIC FREEWAY TRAVEL-TIME PREDICTION WITH PROBE VEHICLE DATA: LINK BASED VERSUS PATH BASED

Short-term travel-time prediction is very important to the real-time traveler information and route guidance system. Various methodologies have been developed for dynamic travel-time prediction. However, most existing studies assume that path travel time is the simple addition of travel times on the consisting links. Through simulation, it is shown that, under recurrent traffic conditions, direct measuring of path-based (or movement-based) travel time rather than link-based travel time could generate a more accurate prediction. Factors that would have an effect on the prediction accuracy are analyzed.

Estimation of Bus Dwell Times with Automatic Passenger Counter Information

The average passenger boarding and alighting times and bus dwell times at stops are important information for estimating transit service capacities. Bus dwell time directly affects vehicle travel time, and thus the fleet size required to provide service based on scheduled headway is affected. Research focused on estimating bus dwell time and the impact of boarding and alighting passengers on dwell time. In addition, the effect of standees, time of day, and service type on bus dwell time was investigated. The data were recently collected from an archived database, within which automatic passenger counter information was recorded. The dwell times and passenger counts were recorded daily during 2001 and the first 6 months of 2002. The bus dwell time and average passenger boarding and alighting time at stops are explained using descriptive statistics.

DETERMINING THE NUMBER OF PROBE VEHICLES FOR FREEWAY TRAVEL TIME ESTIMATION BY MICROSCOPIC SIMULATION

Using probe vehicles to collect real-time traffic information is considered an efficient method in real-world applications. How to determine the minimum number of probe vehicles required for accurate estimate of link travel time is a question of increasing interest. Although it usually is assumed that link travel time is normally distributed, it is shown, on the basis of simulation results, that sometimes this is not true. A heuristic of determining the minimum number of probe vehicles required is developed to accommodate this situation. In addition, the impact of traffic volume on the required probe vehicle number is discussed.

Improving Transit Service Quality and Headway Regularity with Real-Time Control

Disrupted transit operations are often caused by stochastic variations of passenger demand at stations and traffic conditions on service routes, which increase passenger wait times and thus discourage passengers from using the transit system. Efficient, real-time operational control is desirable to maintain headway regularity and reduce the negative effects of service disturbance. A real-time headway control model is proposed to maintain desired headways for pairs of successive vehicles by minimizing total headway variance for all stations in an advanced public transportation system environment, such as an automatic train control system and an automatic vehicle location system. A vehicle’s departure time can be adjusted on the basis of its optimal arrival time at the next station, while considering the maximum attainable operating speeds and the headways to its leading and following vehicles. The proposed real-time control model is tested by simulating a high-frequency light rail transit route in Newark, New Jersey. The simulation results demonstrate that the average passenger wait time is significantly reduced after applying the control model.

INTERMODAL TRANSIT SYSTEM COORDINATION

In urban areas where transit demand is widely spread, passengers may be served by an intermodal transit system, consisting of a rail transit line (or a bus rapid transit route) and a number of feeder routes connecting at different transfer stations. In such a system, passengers may need one or more transfers to complete their journey. Therefore, scheduling vehicles operating in the system with special attention to reduce transfer time can contribute significantly to service quality improvements. Schedule synchronization may significantly reduce transfer delays at transfer stations where various routes interconnect. Since vehicle arrivals are stochastic, slack time allowances in vehicle schedules may be desirable to reduce the probability of missed connections. An objective total cost function, including supplier and user costs, is formulated for optimizing the coordination of a general intermodal transit network. A four-stage procedure is developed for determining the optimal coordination status among routes at every transfer station. Considering stochastic feeder vehicle arrivals at transfer stations, the slack times of coordinated routes are optimized, by balancing the savings from transfer delays and additional cost from slack delays and operating costs. The model thus developed is used to optimize the coordination of an intermodal transit network, while the impact of a range of factors on coordination (e.g., demand, standard deviation of vehicle arrival times, etc) is examined.

Optimal Train Operation for Minimum Energy Consumption Considering Track Alignment, Speed Limit, and Schedule Adherence

Developing an optimal train speed profile for energy-efficient train operation is significant in both theory and applications but very difficult and complex to achieve. An optimization method that minimizes energy consumption by considering track alignment, speed limit, and schedule adherence is proposed. The objective function is total energy consumption, and the decision variables include the timing of train motion regimes. A simulated annealing algorithm (SA) is developed to search for the optimal train operation or “golden run.” The developed model is applied to a segment of the New Haven line of the Metro-North Commuter Railroad, which runs between Woodlawn, New York, and New Haven, Connecticut. A sensitivity analysis is conducted, and the relationship between model parameters and decision variables are explored.

DEVELOPMENT OF A HYBRID MODEL FOR DYNAMIC TRAVEL-TIME PREDICTION

Travel-time prediction has been an interesting research subject for decades, and various prediction models have been developed. A prediction model was derived by integrating path-based and link-based prediction models. Prediction results generated by the hybrid model and their accuracy are compared with those generated by the path-based and link-based models individually. The models were developed with real-time and historic data collected from the New York State Thruway by the Transportation Operations Coordinating Committee. In these models, the Kalman filtering algorithm is applied for travel-time prediction because of its significance in continuously updating the state variables as new observations. The experimental results reveal that the travel times predicted with the path-based model are better than those predicted with the link-based model during peak periods, and vice versa. The hybrid model derives results from the best model at a given time, thus optimizing the performance. A prototype prediction system was developed on the World Wide Web.

DYNAMIC VEHICLE DISPATCHING AT THE INTERMODAL TRANSFER STATION

Transfer time is one of the most important service quality indicators for evaluating intermodal transit systems. In the advent of advanced public transportation systems, vehicle arrival times and transfer demand can be obtained in real time. Thus, the decision of dispatching vehicles at transfer stations can be optimized to reduce the transfer cost. A time-varying total-cost function, including connection delay and missed-connection costs incurred by transfer passengers and vehicle holding cost, is formulated as a function of holding times for vehicles that are ready to be dispatched at transfer stations. A procedure is developed to dynamically optimize the dispatching time for each ready vehicle by minimizing the time-varying objective function. A transit network consisting of four routes connecting at a transfer terminal is designed in this study to demonstrate the application of the dispatching model. The proposed method can be used to advance transit vehicle dispatching strategies and reduce the transfer time.

Optimal All-Stop, Short-Turn, and Express Transit Services Under Heterogeneous Demand

As a major mode choice of commuters, public transit plays an important role in relieving congestion on urban transportation networks. A model is developed for cost-efficient operation that optimizes all-stop, short-turn, and express transit services and the associated frequencies by considering heterogeneous demand. For a transit line with given stations and origin–destination demand, the objective total cost function is optimized by considering a set of constraints that ensure frequency conservation and sufficient capacity subject to operable fleet size. A numerical example is designed that is based on a real-world rail transit line to demonstrate the applicability and effectiveness of the developed model. Results show that optimized integrated service patterns and associated service frequencies significantly reduce total cost.

Predicting Travel Times for the South Jersey Real-Time Motorist Information System

A dynamic travel-time prediction model was developed for the South Jersey (southern New Jersey) motorist real-time information system. During development and evaluation of the model, the integration of traffic flow theory, measurement and application of collected data, and traffic simulation were considered. Reliable prediction results can be generated with limited historical real-time traffic data. In the study, acoustic sensors were installed at potential congested places to monitor traffic congestion. A developed simulation model was calibrated with the data collected from the sensors, and this was applied to emulate traffic operations and evaluate the proposed prediction model under time-varying traffic conditions. With emulated real–time information (travel times) generated by the simulation model, an algorithm based on Kalman filtering was developed and applied to forecast travel times for specific origin-destination pairs over different periods. Prediction accuracy was evaluated by the simulation model. Results show that the developed travel-time predictive model demonstrates satisfactory performance.