Waiting Time Of Passengers Research Articles

Analysing stability and investments in railway networks using advanced evolutionary algorithms

AbstractWe consider a network of periodically running railway lines. Investments are possible to increase the speed and to improve the synchronisation of trains. The model also includes random delays of trains and the propagation of delays across the network. We derive a cost‐benefit analysis of investments, where the benefit is measured in reduced waiting time for passengers changing lines. We also estimate the actual mean waiting time simulating the train delays. This allows us to analyse the impact that an increasing synchronisation of the timetable has on its stability. Simulation is based on an analytical model obtained from queueing theory. We use sophisticated adaptive evolutionary algorithms, which send off avant‐garde solutions from time to time to speed up the optimisation process. As there is a high correlation between scheduled and estimated waiting times for badly synchronised timetables, we are even able to include the time consuming simulation into our optimisation runs.

Modeling of Passenger Waiting Time in Intermodal Station with Constrained Capacity on Intercity Transit

This paper aims to develop a mathematical model to analyze the passenger waiting time in an intermodal station in which intercity transit system served with feeder buses. Passengers are categorized into two groups, named scheduled and random, respectively. The analytic model is developed for quantifying the relationships of passenger waiting time to reliability of feeder bus services and capacity of intercity transit. The numerical results identify that mean waiting time of intercity transit passengers is greater than half of the intercity transit headway, with the condition that feeder service is provided with poor reliability; and on the contrary, mean waiting time will be less than half of the intercity transit headway. Moreover, to avoid long waiting time, headway deviation of feeder buses should be less than 3.5 minutes. In terms of intercity transit capacity, it is suggested that at least 80 % of total arrival passengers should be satisfied.

The impact of the September 11th security fee and passenger wait time on traffic diversion and highway fatalities

This paper investigates the effect of the Security Fee, imposed following September 11, 2001 in the US, and of increased travel delay time, another product of 9/11, on traveler diversion and highway fatalities. Our results are that although both the security fee and the increased travel delay time lead to traffic diversion and increases in highway fatalities, the greater impact is likely to come from travel delays. From a public policy standpoint, this would imply that given various options for public expenditures, monies spent on reducing delays may be more effective than alleviating the security tax burden on passengers.

Dynamic Bus Arrival Time Prediction with Artificial Neural Networks

Transit operations are interrupted frequently by stochastic variations in traffic and ridership conditions that deteriorate schedule or headway adherence and thus lengthen passenger wait times. Providing passengers with accurate vehicle arrival information through advanced traveler information systems is vital to reducing wait time. Two artificial neural networks (ANNs), trained by link-based and stop-based data, are applied to predict transit arrival times. To improve prediction accuracy, both are integrated with an adaptive algorithm to adapt to the prediction error in real time. The bus arrival times predicted by the ANNs are assessed with the microscopic simulation model CORSIM, which has been calibrated and validated with real-world data collected from route number 39 of the New Jersey Transit Corporation. Results show that the enhanced ANNs outperform the ones without integration of the adaptive algorithm.

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.

A procedure to adjust transit trip departure times through minimizing the maximum headway

Transit frequency is usually determined at the heavier load route segment, whereas at other segments the operation may be inefficient due to situations characterized by empty seats. The transit schedulers attempt to overcome this problem by deleting manually some departure times to allow for the initiation of certain trips beyond the beginning of the route and/or termination—before the end of the route. The outcome of this process is the reduction of the number of vehicles required to carry on the timetable. This work describes an automated procedure for the scheduler to adjust the number of departures at each route timepoint to that required from a passenger load standpoint. Given a complete timetable, the procedure reduces the number of departures with the objective to minimize the maximum headway to be obtained, where the headway is associated with passenger wait time. This minimax headway problem is solved through: 1. (i) representation of the problem on a directed network with a special pattern; 2. (ii) applying a modified shortest-path algorithm on the network to determine the minimax headway; and 3. (iii) applying an algorithm to ensure that the exact number of required departures will be included in the optimal solution. Finally, some computational results and an example are demonstrated.

Research contributions to managing transit service reliability

AbstractService reliability problems are a major concern shared by transit system users and operators. Unreliable service leads to additional vehicle requirements and higher operating costs; passengers suffer increased wait time and higher travel time uncertainty, leading to a general dissatisfaction with service and the possibility of seeking other means of transport. Because of the significance of this problem, considerable research attention has been focused in recent years on developing ways to improve service reliability.This paper examines contributions which have been made to managing transit service reliability. This topic encompasses the subjects of vehicle run time, run time variation, headway variation, passenger wait time, and reliability control. Both theoretical and empirical approaches have been undertaken, and each has contributed to our knowledge in addressing the overall problem. Significant contributions to the state‐of‐the‐art are reported and the implications on present practice are discussed.

Optimal control of headway variation on transit routes

AbstractHeadway control strategies have been proposed as methods for correcting transit service irregularities and thereby reducing passenger wait times at stops. This paper addresses a particular strategy which can be implemented on high frequency routes (headways under 10–12 minutes), in which buses are held at a control stop to a threshold headway. An algorithm is developed which yields the optimal control stop location and optimal threshold headway with respect to a system wait function. The specification of the wait function is based on the development of several empirical models, including a headway variation model and an average delay time model at control stops.A conclusion is reached that the headway variation does not increase linearly along a route, a common assumption made in many previous studies. Furthermore, the location of the optimal control stop and threshold value are sensitive to the passenger boarding profile, as expected. The algorithm itself appears to have practical application to conventional transit operations.

The allocation of buses in heavily utilized networks with overlapping routes

Many transit systems outside North America are characterized by networks with extensively overlapping routes and buses frequently operating at, or close to, capacity. This paper addresses the problem of allocating a fleet of buses between routes in this type of system; a problem that must be solved recurrently by transit planners. A formulation of the problem is developed which recognizes passenger route choice behavior, and seeks to minimize a function of passenger wait time and bus crowding subject to constraints on the number of buses available and the provision of enough capacity on each route to carry all passengers who would select it. An algorithm is developed based on the decomposition of the problem into base allocation and surplus allocation components. The base allocation identifies a feasible solution using an (approx.) minimum number of buses. The surplus allocation is illustrated for the simple objective of minimizing the maximum crowding level on any route. The bus allocation procedure developed in this paper has been applied to part of the Cairo bus system in a completely manual procedure, and is proposed to be the central element of a short-range bus service planning process for that city.

Service frequency, schedule reliability and passenger wait times at transit stops

A model is developed to evaluate the sensitivity of expected passenger wait time at transit stops to service frequency and schedule reliability. This model represents an advance over previous models because it explicitly incorporates a passenger decision-making process, rather than assuming that passengers arrive at random instants in time. The model is tested against more traditional models using data from the Chicago area. These tests indicate that the passenger-choice model represents a significant improvement in predictive ability. The implications of this model are that passenger wait time is much more sensitive to schedule reliability and much less sensitive to service frequency than previously believed.