Simulation-Based Estimates of Delays at Freeway

ABSTRACT Work zone related traffic delay is an important cost component on freeways with maintenance activities. This study demonstrates that delays are always underestimated by using the deterministic queuing theory. Computer simulation is a valuable approach of estimating delay under variety of existing and future conditions. However, a single simulation run, which can be quiet costly in terms of both computer and analyst time

Development of a Partial Proportional Odds Model for Pedestrian Injury Severity at Intersections

Pedestrian injury in crashes at intersections often results from complex interaction among various factors. The factor identification is a critical task for understanding the causes and improving the pedestrian safety. A total of 2,614 crash records at signalized and non-signalized intersections were applied. A Partial Proportional Odds (PPO) model was developed to examine the factors influencing Pedestrian Injury Severity (PIS) because it can accommodate the ordered response nature of injury severity. An elasticity analysis was conducted to quantify the marginal effects of contributing factors on the likelihood of PIS. For signalized intersections, seven explanatory variables significantly affect the likelihood of PIS, in which five explanatory variables violate the Proportional Odds Assumption (POA). Local driver, truck, holiday, clear weather, and hit-and-run lead to higher likelihood of severer PIS. For non-signalized intersections, six explanatory variables were found significant to the PIS, in which three explanatory variables violate the POA. Young and adult drivers, senior pedestrian, bus/van, divided road, holiday, and darkness tend to increase the likelihood of severer PIS. The vehicles of large size and heavy weight (e.g. truck, bus/van) are significant factors to the PIS at both signalized and non-signalized intersections. The proposed PPO model has demonstrated its effectiveness in identifying the effects of contributing factors on the PIS.</p

Predicting Bus Travel Time with Hybrid Incomplete Data – A Deep Learning Approach

The application of predicting bus travel time with real-time information, including Global Positioning System (GPS) and Electronic Smart Card (ESC) data is effective to advance the level of service by reducing wait time and improving schedule adherence. However, missing information in the data stream is inevitable for various reasons, which may seriously affect prediction accuracy. To address this problem, this research proposes a Long Short-Term Memory (LSTM) model to predict bus travel time, considering incomplete data. To improve the model performance in terms of accuracy and efficiency, a Genetic Algorithm (GA) is developed and applied to optimise hyperparameters of the LSTM model. The model performance is assessed by simulation and real-world data. The results suggest that the proposed approach with hybrid data outperforms the approaches with ESC and GPS data individually. With GA, the proposed model outperforms the traditional one in terms of lower Root Mean Square Error (RMSE). The prediction accuracy with various combinations of ESC and GPS data is assessed. The results can serve as a guideline for transit agencies to deploy GPS devices in a bus fleet considering the market penetration of ESC

Optimal Service Planning for a Sustainable Transit System

A mathematical model and methodology are presented in this paper that can be used to determine the sustainability of a bus service. To formulate the optimization model, an entire bus route in a suburban area is considered on which many eligible stop locations are distributed realistically as discrete points such as intersections or entrances to housing developments. The objective total profit function is maximized by optimizing the number and locations of stops, the headway, and the fare. The number of passengers for the service is dependent on passengers’ access distance, wait time, in-vehicle time, and fare. The solution methodology is applied to an example that uses a bus route in suburban Woodbridge, NJ to demonstrate its effectiveness. The sensitivity of the total profit and of the amount of passengers served to various parameters is analyzed

Feasibility of shoulder use for highway work zone optimization

Highway maintenance, often requiring lane closure, is very expensive in terms of the costs associated with transportation agencies (i. e. work zone setups) and road users (i. e. delay). Longer work zones tend to increase the user delay but will be efficient because of fewer repeated setups. To increase road capacity and mitigate congestion impact for a short-term work zone, temporary shoulder use may be applied. This study develops an analytical model to optimize work zone length on a multi-lane highway considering time-varying traffic volume and road capacity affected by light condition, heavy vehicle percentage, and lane width. The results can be used to evaluate the work zone impact (i. e. delay and cost) and assist engineers/planners to prepare and develop a cost-effective highway maintenance plan. A case study for a highway work zone in New Jersey has been conducted, in which the optimized solution is found. A guideline of using road shoulder under various circumstances is developed

Identifying factors and mitigation technologies in truck crashes in New Jersey.

Transportation Department, Office of Research and Special Programs, Washington, D.C.New Jersey Department of Transportation, TrentonMode of access: Internet.Author corporate affiliation: New Jersey Institute of Technology, National Center for Transportation and Industrial Productivity, Newark""September 2003."Includes bibliographical referencesFinal report. Jan. 2002-Sep. 2003Subject code: JASubject code: VNSubject code: JLKSubject code: JLIFSubject code: DEFSubject code: K

Joint Optimization of Bus Size, Headway, and Slack Time for Efficient Timed Transfer

The level of transit service may be elevated by efficient timed transfer, which reduces travel time and increases productivity. However, timed transfer may be costly because of the stochastic nature of vehicle arrivals. A mathematical model is developed to minimize the total cost (e.g., operator and user costs) of a transfer hub that consists of multiple transit routes, subject to capacity constraints. The decision variables, including bus size, headway, and slack time, are jointly optimized through consideration of various levels of coordination. A numerical example is given to demonstrate the applicability of the developed model

Optimizing fare and headway to facilitate timed transfer considering demand elasticity

Fare and service frequency significantly affect transit users’ willingness to ride, as well as the supplier\u27s revenue and operating costs. To stimulate demand and increase productivity, it is desirable to reduce the transfer time from one route to another via efficient service coordination, such as timed transfer. Since demand varies both temporally and spatially, it may not be cost-effective to synchronize vehicle arrivals on all connecting routes at a terminal. In this paper, we develop a schedule coordination model to optimize fare and headway considering demand elasticity. The headway of each route is treated as an integer-multiple of a base common headway. A discounted (reduced) fare is applied as an incentive to encourage ridership and, thus, stimulate public transit usage. The objective of the proposed coordination model is used to maximize the total profit subject to the service constraint. A numerical example is given to demonstrate the applicability of the proposed model. The results show that the optimized fare and headway may be carefully applied to yield the maximum profit. The relationship between the decision variables and model parameters is explored in the sensitivity analysis