A Simulation Model and Experiments

There are diverse methods and tools aimed to support public transport operators regarding routes, timetables and vehicle schedules. This set includes passenger surveys, land-use models, field tests, heuristics, operations research techniques and computer simulations. Computer simulations offer a feasible, flexible and attractive tool for planning and analysing transit systems. Transit simulations may serve several interests (Meignan et al., 2007): global observation of the network to check its functioning and design; evaluation and control of dynamic processes (e.g. transfer synchronization); evaluation of the network efficiency using various measures for different alternatives (e.g. routes or frequencies). In order to adequately analyse and evaluate the performance of real-time transit systems, it is essential to model dynamically the interaction between passenger demands and transit operations. Transit simulations provide a dynamic perspective on transit operations, enabling representations of complex interactions between the transit system components; transit vehicles, passengers, and transit operations. Service reliability is one of the main factors determining transit system level of service, as unreliable service results in longer waiting times, uneven passengers loads and missed transfers. There are several sources of variability that contribute to service unreliability, including variability in arrival/departure times to/from bus stops, dwell time and riding times. Studying the effect of transit operations on passengers is difficult because of interrelated stochastic process involved. Many studies in this area assumed a constant passenger arrival rate and neglected transit vehicle capacity. A simulation- based evaluation model of real-time transit services was developed to improve service reliability and enable the representation of large scale transit systems (Shalaik, B. et al, 2012b). This model is used to evaluate a real-time transit system in a real-world bus line in Blackpool city under various scenarios.

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Our simulation model, like all models, requires a set of input data and generates output data. The model is stochastic where different runs with the input data will generate different output results. The model requires transit-related data in order to simulate the public transport system. This input includes:

1- Routes: every transit vehicle is assigned to a unique route which serves a sequence of stops. Bus route information is derived from our real-time bus tracking system.

2- Timetables: the service schedule is published to the passengers and presents the expected arrival/departure time for each bus trip in each bus stop.

3- Demand: Passenger demand, which determines the alighting and boarding, is represented by two components: the demand to get on and the demand to get off each bus at each stop. On high frequency routes passengers arrive randomly at bus stops (Ronghui L., and Shalini, 2007) and in the simulation process random numbers were generated to reflect this. The passengers demand varies by one variable (the bus stop type). If bus stop is a high demand one, the generated number of passengers should be higher than when the stop is a regular stop.

4- Characteristics – Transit vehicles and bus stops have special characteristics that influence transit operations. Features, such as bus capacity are important

for a transit service and so we assume that maximum bus capacity is 50 passengers. Regarding the type of bus stop, there are regular bus stops and time point bus stops

The transit simulation model reported in this thesis is transit operations oriented and therefore focuses more on the supply side than on the demand side. Transit vehicles move between bus stops within the network of public transport, these vehicles arrive at the stops at different times dropping off and loading passengers. Elements of the behaviour of these vehicles that are modelled include; generation of vehicles based

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on schedules, behaviour at stops, and a detailed representation of passenger demand at the various stops. Every transit analysis tool has to assume some characteristics (function, distribution) on the transit service mechanisms; boarding, and alighting processes, running times, departure and delay times. The basic attributes of transit operation as travel time, boarding, and alighting processes are crucial for any model that intends to represent transit operations. These assumptions are in the core of every model because they dictate the measures of service (e.g. passenger waiting times (Bowman and Turnquist, 1981). The movement of the buses is derived from our real-time bus tracking system. The simulation model interface is shown in Figure (4-39). A record is generated every time a transit vehicle exits a bus stop. Every record includes stop ID, Vehicle no, arrival time, scheduled time, waiting passengers, boarding passengers, alighting passengers, occupancy, and passengers left behind.

Figure 4-39 Simulation model interface

By modelling the passenger flow and measuring transit vehicle performance indicators, we can analyse the overall performance of the network and enable the operators to fix and maintain any problem that could occur. Passengers take a certain amount of time at bus stops waiting for the next bus, or queuing for boarding and

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alighting (Rodrigo, .F and Nick, T., 2005). This time is called Passenger Service Time (PST) and it is a function of the number of passengers. The simulation model was developed to allow visualising what would happen at a transportation network under a variety of operating conditions. The aim of this model is to analyse the overall results of passenger service time due to changes to certain bus operational factors. The same set of variables and parameters were used for all the experiments. The initial conditions assumed the following:

1) Buses carry passengers up to their capacity, 2) Other passengers are waiting at bus stops, and

3) Buses are running according to their planned timetable and headways.

The proposed simulation model can run different experiments with different scenarios that simulate passenger flow in a real-world environment with disruptions such as trip delays, miss-connection or cancellation of buses along a route. There are three different levels or stages where passengers can be affected by transit services. These stages are the origin-point stage, boarding stage and arrival stage.

 Origin-point stage

The origin point stage is where the passengers wait for the next bus, with the possibility of deviation from the advertised timetable (delayed or ahead). At this stage, passengers might have to wait longer than the expected time (Excess Waiting Time) due to irregular headway or a bus that fails to run. When a bus arrives at a bus stop, there is a possibility that some or all passengers cannot board because of the bus’s limited capacity, and this will have a negative impact on their travelling time, causing more delay and increasing waiting time at other bus stops.

 Boarding stage

The boarding stage is the stage where the PST is a function of passenger demand. The boarding passengers’ wait-time must include an allowance for passengers on the bus to alight.

 Arrival stage

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arrival can be on time, ahead or delayed depending on the deviation from the timetable.