A METAHEURISTICS BASED SIMULATION TOOL

TO OPTIMIZE DEMAND RESPONSIVE

TRANSPORTATION SYSTEMS

Eneko Osaba, Pablo Fernandez

Mobility Unit, Deusto Technology Center, Av. Universidades, 24, Bilbao, Spain

Roberto Carballedo, Asier Perallos

Department of Software Engineering, University of Deusto, Av. Universidades, 24, Bilbao, Spain

Keywords: Demand simulation tool, Intelligent transport, Demand responsive transport, Evolutionary computing.

Abstract: Public passenger transport is an important area that affects our quality of life. The design of routes and

frequencies of such systems is very important to ensure their economic viability. The work presented in this

paper focuses on the design of a software tool that assists in the creation of routes and schedules of

passenger transport systems. For this, we have developed an application that is based on evolutionary

computing techniques to simulate passenger demand and adjust the routes and frequency of the services to

meet those demands. The result of work done is a software tool, and a metaheuristic algorithm that can be

used for solving optimization problems.

1 INTRODUCTION

There are different kinds of public transportation

systems, each one with its own features but they all

share some disadvantages. One major disadvantage

of the conventional transportation systems is the lack

of resources to satisfy all the users’ demands. In

rural areas and places with low demographic

density, the existence of regular public

transportation systems is not profitable. There are

some areas where public transportation vehicles do

tours and stops without picking or delivering any

passenger. If this situation is constantly repeated the

route will eventually be eliminated. Whenever the

supply of public transport services in an area is

reduced, we could say that the quality of life of

people living in that region decreases. To avoid this

loss of quality of life, we should analyze the demand

of passengers, and to adapt transportation systems to

passenger demand. This is essentially the concept of

transport systems on demand.

Transportation-On-Demand (TOD) (Jorgensen,

Larsen and Bergvinsdottir, 2007) is concerned with

the transportation of passengers or goods between

specific origins and destinations at the request of

users. Most TOD problems are characterized by the

presence of three often conflicting objectives:

maximizing the number of requests served,

minimizing operating costs and minimizing user

inconvenience. As is common in many

combinatorial optimization problems, these

objectives are conflicting and it is needed to sort

them by importance.

Many of the techniques used to solve these

problems do not yield an exact solution. This is

because the types of problems to be solved are

classified as NP-hard (Garey and Johnson, 1990).

For this reason, heuristics techniques are used for

obtaining good approximations.

In this work an algorithmic solution to this

problem has been developed. This solution consists

in a hybrid algorithm combining two evolutionary

computing techniques: a Genetic Algorithms and a

Simulated Annealing algorithm.

Besides the algorithm, a simulation tool has been

developed. The tools is oriented to the bus public

transport, it allows the user to create virtual

environments with dynamic bus stations, stops and

clients requests. The tool is able to obtain the best

route through several bus stations that satisfies

384

Osaba E., Fernandez P., Carballedo R. and Perallos A..

A METAHEURISTICS BASED SIMULATION TOOL TO OPTIMIZE DEMAND RESPONSIVE TRANSPORTATION SYSTEMS.

DOI: 10.5220/0003499803840389

In Proceedings of the 6th International Conference on Software and Database Technologies (ICSOFT-2011), pages 384-389

ISBN: 978-989-8425-77-5

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

passengers’ requests. The design of the route is

made by means of the hybrid algorithm mentioned.

The aim of the new tool is to support the

decision to create or delete a particular route. The

developed simulation tool has also been used to

validate the algorithmic solution proposed.

This paper is structures as follow: the first

section makes a brief state-of-the-art about the most

common transportation problems and the existing

traffic simulation systems. Then the simulation tool

is presented as well as the proposed algorithms. The

final solution is presented in the next section and the

development of the hybrid algorithm is explained.

Finally the conclusions and the future work is

presented.

2 CONTEXT

2.1 Transportation Problems

Most of the problems arisen in transportation on

demand topic have similar characteristics, which

means that they can be framed as instances of other

generic and well know problems. In this section, we

present the most common traditional problems in the

field of transportation on demand.

Travelling Salesman Problem (TSP)

(Applegate, Bixby, Chvátal and Cook, 2006): The

Travelling Salesman Problem (TSP) is an NP-hard

problem in combinatorial optimization studied in

operations research and theoretical computer

science. Given a list of cities and their pair-wise

distances, the task is to find a shortest possible tour

that visits each city exactly once. This type of

problem is used as a benchmark for many

optimization algorithms.

Vehicle Routing Problem (VRP) (Dantzig and

Ramser, 1959): The vehicle routing problem (VRP)

is a generalization of the TSP. The aim of the

problem is to service a number of customers with a

fleet of vehicles. Often the context of this type of

problem is related to deliver goods located at a

central depot to customers which have placed orders

for such goods. Implicit is the goal of minimizing

the cost of distributing the goods. Many variants of

the VRP are described in the literature (Pisinger and

Ropke, 2007). These problems include the addition

of variables and constraints. One of the most popular

variants includes time windows for deliveries. These

time windows represent the time within which the

deliveries (or visits) must be made (Repoussis,

Tarantilis and Ioannou, 2009).

Demand Responsive Transport (DRT):

Demand Responsive Transport or Demand-

Responsive Transit (DRT) or Demand Responsive

Service is an advanced, user-oriented form of public

transport. It is characterized by flexible routing and

scheduling of small/medium vehicles operating in

shared-ride mode between pick-up and drop-off

locations according to passengers needs. DRT

systems provide a public transport service in rural

areas or areas of low passenger demand, where a

regular bus service may not be economically viable.

DRT systems are characterized by the flexibility of

the planning of vehicle routes. These routes may

vary according to the passenger’ needs in real time.

This is the type of problem that we used to

benchmark the algorithm proposed in this paper.

2.2 Traffic Simulation Tools

The simulation tools help us to measure the

performance of a system in a virtual environment.

This is a usual practice before making a large scale

change in an existing platform because there are

involved a lot of costs associated with this process.

Another major feature of the simulation tool is the

ability of predicting the behaviour of one

environment in a specific context.

Talking about simulation systems in the

transportation context is talking about prediction and

traffic optimization. One of the main goals in these

simulations systems is predicting the effects and the

impact of a vehicle or road accident in the regular

traffic as stated in (Burghout, Koutsopoulos, and

Andreasson, 2010), this is a critical fact in places

where the traffic density is high because a vehicle

accident can cause several inconveniences to the

users of the road.

Another important goal of the transportation

related simulation systems is the traffic light

calibration. Thanks to the traffic simulators the

agents are able to setup the traffic lights according to

the simulated traffic and other natural factors like

the weather or the time of the day as written in

(Lopez-Mellado, 2010), in order to optimize the

light cycles.

3 SIMULATION TOOL

Nowadays there are many kinds of public

transportation systems: on demand or regular ones.

This requires the public transport to be more

demanding and sophisticated. But before performing

variations to the regular service, by adding more

A METAHEURISTICS BASED SIMULATION TOOL TO OPTIMIZE DEMAND RESPONSIVE TRANSPORTATION

SYSTEMS

385

transport units, new stops or creating new routes or

lines, it is necessary to make preliminary studies to

with the objective of improving the quality of

service and the client satisfaction without making

mistakes. To achieve this excellence level, different

techniques and tools are used and applied before

deploying the changes, in order to guarantee that the

people in charge will be able to have an idea of the

impact of the new variations.

In this work it has been developed a simulation tool,

oriented to bus public transport system (on demand

or regular), in rural or urban environments. With this

tool, different tests can be made to check the

performance improvement when adding or removing

a new stop to/from a route, or the creation of a new

line.

With this tool the user will be able to create

different environments composed of stations, that

user may place wherever he desires. The application

will calculate the optimized route to navigate

through all the placed stations using the artificial

intelligence algorithm developed that will be further

explained later. Once the route is created, the user

may make requests, which the system will manage

on an efficient way. The system will also be able to

make a simulation of how the bus would be

completing the route and managing the dynamic

requests made in real time.

It’s important point out the difference between

primary and secondary stations. The primary stations

are the ones the bus must pass through, the

secondary are the ones that will only be part of the

route if it exists enough demanding from the users.

This feature allows to create on demand transport

lines, and helps to select which stations will be

primary or secondary.

Figure 1, shows an image of main interface of

the tool developed. That image represents an

environment composed by some primary and

secondary stops.

Figure 1: Deployed Tabs.

Figure 2: Main window.

Figure 2, shows the application’s main screen.

Several deployable tabs can be seen. These tabs

contain diverse information. The top ones for

example, have information about the route of each of

the buses and the history of the active bus. The

bottom one contains a history of all the requests

made, a panel to make transportation requests and

some controls to manage the simulation of the bus

tour.

Apart from this, a mobile application has been

developed. Thanks to this, the users can kwon the

active bus route, in map or text format. Furthermore,

it also offers the possibility of making requests in the

same way as the web application.

To finish, in Figure 3 a conceptual schema of the

final system architecture is shown.

Figure 3: System architecture.

As mentioned before, there exist two ways to

use the simulation tool. One is based on a web

ICSOFT 2011 - 6th International Conference on Software and Data Technologies

386

application and the other one is an android based

mobile application. These two alternatives access to

a central server via internet, this server can be

divided into three different components. The first

one is a database where all the information about the

made requests, the active routes and the statistics are

stored. The second component is composed by some

web services that offer several access methods to the

database and business logic. The web services are

consumed by some Java Server Pages (JSP), the

third component of the server. These JSP pages

represent the presentation layer and user access to

both web application and mobile application.

4 PROPOSED ALGORITHMS

As mentioned in the preceding section, the

implemented simulation tool uses an artificial

intelligence algorithm as base. This algorithm is

used to resolve the route planning problem, in a

static way in regular transport lines, as well as in a

dynamic way, in on demand transport lines. In

application level, the algorithm will be in charge of

finding in every moment the optimized route the bus

has to go through to visit all the stations of the

environment. To implement the solution, the

problem has been treated as a DRT one, as

mentioned in the introduction of the article, and

resolved with a heuristic method that obtains good

approximations. To perform this task, we designed a

hybrid algorithm that combines simulated annealing

methods and genetic algorithms. Then we explain

the details of each technique separately.

Simulated Annealing (Rutenbar, 1989): This is

one of the most popular local search techniques. It is

based on the physical principle of cooling metal.

Using that analogy, it generates an initial solution

and the process proceeds by selecting new solutions

randomly. The new solutions are not always better

than the initial solution, but as time passes and the

temperature decreases (the metal becomes stronger),

each new solution must be better than previous

solutions.

Genetic Algorithm (Zhang, Yao and Zheng,

2009): This algorithm is inspired by the laws of

natural selection and the evolution of the animal

species. An initial population of solutions is defined.

This initial population consists of a number of

individuals (solutions of the problem.) Then, with

the combination and evolution of these individuals,

the algorithm tries to get a better solution.

Hybrid Algorithm (Kaur and Murugappan,

2008): The hybrid algorithm is the resultant from

joining the genetic and the simulated annealing

algorithms. It was decided to use this algorithm after

a test period detailed in the following section.

5 TEST AND FINAL SOLUTION

In the field of artificial intelligence, when you create

a new algorithm, or modify an existing one, results

that show that the new solution is better than other

known solutions have to be submitted. To do this,

there are sets of problems used to compare results in

computational resources and parameters related to

the quality of the solution. In our case, there is not a

set of sample problems for comparing the

performance of design algorithms for demand

responsive transport routing problems. For this

reason, we have defined our own set of testing, and

we are going to make comparisons between our

algorithm, a brute-force optimal algorithms and each

of the techniques used in our hybrid algorithm.

As indicated above, for the design of our hybrid

algorithm, separate versions of simulated annealing

and a genetic algorithm have been implemented. In

addition, we have implemented a "brute force"

algorithm, to find out the optimal solution for small

instances of the problem (with few intermediate

stops).

With these 3 algorithms, there have been a series

of tests to measure the performance of each

algorithm and the ability of each one to solve the

problem. As a result of these tests, we have obtained

several conclusions:

1. The “brute force” algorithm is optimal because

it always finds the best solution. Even so, it has

the disadvantage that the execution time is

unacceptable when the number of stations

increases to more than 9 (for a large number of

stations cannot even get a solution). This

algorithm cannot be used in a real scenario.

2. The simulated annealing algorithm only finds

the optimal solution when the first and last

station does not vary during the resolution

process. Running time is always the same

regardless of the number of stops.

3. In the case of genetic algorithm, the execution

time is constant if the number of generations is

also constant. An advantage of this algorithm is

that the probability of finding a good solution

is independent of the number of stops.

After preliminary analysis of algorithms

separately, we came to the conclusion that the results

of runtime and solution quality were not good. For

this reason we decided to combine the two

A METAHEURISTICS BASED SIMULATION TOOL TO OPTIMIZE DEMAND RESPONSIVE TRANSPORTATION

SYSTEMS

387

heuristics.

5.1 Our Hybrid Algorithm

Our hybrid algorithm came up with the aim to

combine the advantages of genetic algorithms and

simulated annealing:

 Rapid and constant execution time (simulated

annealing).

 Probability of finding a good solution for the

problem instances with many stops (genetic

algorithm).

The solution would avoid the main drawback of the

two algorithms:

 The solution should be optimal or very close

to it.

With all these goals, it thought about making the

hybrid. By nature of the two algorithms, it is

appropriate to insert the execution of simulated

annealing algorithm in the execution of genetic

algorithm. That is because the first algorithm is

focused on only one solution and the second works

simultaneously with different solutions.

Having decided the model of integration, there

were two options to do the integration:

 Integrate the simulated annealing in the

process of creating the initial population.

 Integrate the simulated annealing in the

process of reproduction, right after generating

the new population.

After several tests, we concluded that the most

effective solution was to apply the simulated

annealing algorithm just after the reproduction

process. Below is a table showing the results of the

tests. The table shows the number of stops, the

number of generations used in the genetic algorithm,

runtime, and the percentage of times the algorithm

finds the optimal solution.

Table 1: Results of the tests.

Stations Generations T. of execution

% of optimal

solution

9 5 3 seconds 80%

9 10 5 seconds 100%

10 5 3 seconds 80%

10 10 5 seconds 100%

11 5 3 seconds 80%

11 10 5 seconds 100%

Comparing the proposed alternative with each of

the separate algorithms, we can ensure that the

execution time is right, regardless of the number of

stops. Moreover, in situations where the optimal

solution is not found, the average deviation for the

optimal solution does not exceed 3% of the value of

the optimal solution.

6 CONCLUSIONS AND FURTHER

WORK

The work presented is the result of a research project

funded by the Basque government. The project

focuses on the design of a software tool that assists

in the creation of routes and schedules of passenger

transport systems. For this, we have developed an

application that is based on evolutionary computing

techniques to simulate passenger demand and adjust

the routes and frequency of the services to meet

those demands. The result of work done is a

software tool, and a metaheuristic algorithm that can

be used for solving optimization problems.

A part of the results of this project, and as there

is no benchmark problems for this type of

transportation problem we are currently developing

a framework for algorithm validation. This new

framework will serve to validate the performance of

demand responsive routing algorithms. During the

design of this benchmark problems we will define

the basic criteria to measure algorithms

performance. Examples of such criteria are running

time, solution quality, ease of implementation,

robustness and flexibility.

REFERENCES

R M Jorgensen, J Larsen and K B Bergvinsdottir: Solving

the Dial-a-Ride problem using genetic algorithms.

Journal of the Operational Research Society, 58, pp.

1321–1331. 2007

Garey, M. R. and Johnson, D. S: Computers and

Intractability; a Guide to the Theory of Np-

Completeness. W. H. Freeman & Co. 1990.

Applegate, D. L.; Bixby, R. M.; Chvátal, V.; Cook, W. J.

The Traveling Salesman Problem. ISBN 0691129932.

2006

Dantzig, G. B.; Ramser, J. H. The Truck Dispatching

Problem. Management Science Vol. 6, No. 1, October

1959, pp. 80-91.

D Pisinger, S Ropke: A general heuristic for vehicle

routing problems. Computers and Operations

Research, Volume 34 Issue 8, August, 2007.

Repoussis, P. P.; Tarantilis, C. D.; Ioannou, G.: Arc-

guided evolutionary algorithm for the vehicle routing

problem with time windows. IEEE Transactions on

Evolutionary Computation, Volume 13 Issue 3, June

2009.

Burghout, Wilco; Koutsopoulos, Haris N; Andreasson,

Ingmar: Incident Management and Traffic Information

Tools and Methods for Simulation-Based Traffic

ICSOFT 2011 - 6th International Conference on Software and Data Technologies

388

Prediction. Transportation Research Record, Issue

2161, pp. 20-28, 2010

Lopez-Mellado Ernesto: A modeling framework for urban

traffic systems microscopic simulation. Simulation

Modelling Practice and Theory, Volume 18, Issue 8

pp. 1145-1161, Sep 2010

Rutenbar, R. A.: Simulated Annealing algorithms: an

overview. IEEE Circuits and Devices Magazine 5 (l),

pp. 19-26. 1989

L Zhang, M Yao, N Zheng: Optimization and

improvement of Genetic Algorithms solving Traveling

Salesman Problem. Image Analysis and Signal

Processing, pp. 327-332 April 2009.

Kaur, D. Murugappan, M. M: Performance enhancement

in solving Traveling Salesman Problem using hybrid

genetic algorithm. Fuzzy Information Processing

Society, pp. 1-6, May 2008.

A METAHEURISTICS BASED SIMULATION TOOL TO OPTIMIZE DEMAND RESPONSIVE TRANSPORTATION

SYSTEMS

389