If a rider takes two buses to get to work, it counts as two boarding rides.

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Abstract This paper defines bus timetables setting problem during each time period divided in terms of passenger flow intensity; it is supposed that passengers evenly arrive and bus runs are set evenly; the problem is to determine bus runs assignment in each time period to minimize the total waiting time of passengers on platforms if the number of the total runs is known.

For such a multistage decision problem, this paper designed a dynamic programming algorithm to solve it. Global optimization procedures using dynamic programming are developed. Introduction The transit planning process includes four basic components which Bus frequency determination using passenger count usually performed in sequence: The operation period is divided into several subperiods for which a specific number of trips are determined.

Trip frequency scheduling is more or less identical to headway determination and hence to the so-called timetable construction problem. The timetable problem, however, requires the specification of precise arrival and departure times at terminals and major stops.

Furth and Wilson [ 2 ] proposed a model which allocates the available buses between time periods and between routes so as to maximize net social benefit subject to constraints on total subsidy, fleet size, and levels of vehicle loading; however, they did not take minimizing the waiting time of passengers into account.

De Palma and Lindsey [ 3 ] considered the transit network timetabling problem on a single transit link, where each transit rider was assumed to incur a varying schedule delay cost from travelling earlier or later; but in their research it was supposed that vehicle capacity constraints were ignored, so that a vehicle can carry any number of passengers without congestion, which is inconsistent with the reality.

Some literatures Wan and Lo, [ 4 ]; Barra et al. As a continuation of research, he provided alternative methods for constructing bus timetables using passenger load data Ceder, [ 12 ]. Ceder [ 13 ] described an automats procedure for the scheduler to adjust the number of departures at each route timepoint to that required from a passenger load standpoint with the objective to minimize the maximum headway to be obtained.

Van Oudheusden and Zhu [ 14 ] developed an integer programming model for trip frequency scheduling and presented two heuristic solution methods, one of which was based on linear programming and the other was a straightforward derivation of common bus operation practice; but they could not guarantee the global optimal solution.

Zhao and Zeng [ 17 ] presented a metaheuristic method for optimizing transit networks, including route network design, vehicle headway, and timetable assignment; a metaheuristic search scheme that combines simulated annealing, tabu, and greedy search methods was presented.

The majority of previous solution methods for transit frequencies and timetable setting problems relied on problem-based heuristics or design guidelines, which could not guarantee the best solution in mathematics.

This paper aims to optimize arrangement of bus runs on a single line, regards the objective problem as a multistage decision problem, and tries to look for the global optimal solution through dynamic programming to obtain the best scheme of bus runs arrangement.

The remainder of the paper is organized as follows. Section 2 describes the bus timetable setting problem. Section 3 develops an optimization procedure with dynamic programming to solve the bus timetable setting problem.

In Section 4a numerical experiment is given to demonstrate the efficiency of the proposed method. The final section concludes the paper and discusses future research issues. The Bus Timetable Setting Problem 2. Time Period Division A bus line has up and down going directions; this paper only studies the case of one direction ; see Figure 1.

Of course, the same analysis can be made for the reverse direction. A single bus line. Here, it is supposed that are kept fixed and that the earliest passengers occur at stop 1 at timepoint. Since a travel time is needed for buses getting to the next stops, the timepoints of passengers beginning to accumulate at the next stops can be regarded as stop 1stop 2.Abstract: Aiming at decreasing average passenger wait time at stops, headway adherence is used to measure the route-level service reliability for high frequency bus route services.

Considering the fluctuation of passenger demand and the stochastic nature of running time on road segments, a fixed route of high-frequency bus services was set to be a case study. Identify top companies for sales and analysis purposes.

Market Studies. Frequency of passenger transport usage in the United Kingdom (UK) , by age group Number of buses in use as. In this report, you can get a hard-hitting analysis of Automatic Passenger Countor principals, participants, Automatic Passenger Countor geological areas, product type, and Automatic Passenger Countor end-user’s applications.

Their combined citations are counted only for the first article. Merged citations. This "Cited by" count includes citations to the following articles in Scholar. The ones marked * may be different from the article in the profile. Bus frequency determination using passenger count data.

A Ceder. Transportation Research Part A: General 18 ( The main objective of Discrete Dynamics in Nature and Society is to foster links between basic and applied research relating to discrete dynamics of complex systems encountered in the natural and social sciences.

“Bus frequency determination using passenger count data,” Transportation “Genetic algorithm for bus frequency.

Ceder, A Bus frequency determination using passenger count data Transportation Research 18A Google Scholar Ceder, A a Operational objective functions in designing public transport routes Journal of Advanced Transportation 35 Google Scholar.

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Global Optimization for Bus Line Timetable Setting Problem