0LAUK0 2015 01 Design Report

Quick Links

Home
Content	Plan	Report	Simulation
Log	Week1	Week2	Week3	Week4	Week5	Week6	Week7

Autonomous Bus Scheduling System Design Report

Introduction

Background

Public transportation has been around for a couple of decades now. It started with the stagecoach in the nineteenth century. Later on came the steam trains which were capable of carry large capacity of people at the same time. The trains were too big to travel in city themselfs, so the tram was invented. However not city are capable of creating a tramrail network. So with the increasing demands of mobility in the sixties, busses started to be used for public transportation in cities around the Netherlands and the rest of europe.

Bus stops were created around cities and a static scheduling system was implemented. This static scheduling system uses a fixed time table around the city. The busses follow fixed routes each time and do not take into account if there are any people waiting at a bus stop or not. Meaning that the busses do not take a shorter route when nobody is willing to stop of is waiting a certain bus stop. This means it is time for a dynamic system, this will be the main focus in this report. The new scheduling system for busses aims to create a more efficient passenger flow. It will be able to send extra busses to bus stops that are or are getting too crowded and also the system will choose a shorter route to it’s destination when no passengers are waiting at the coming bus stops. Users can check in at the bus stop or with their mobile phone apps. The scheduling system will then optimize the routes by taking this information into account. A small part of the report will discusses the possibility of using autonomous busses in the new dynamic scheduling system, analyzing the impact of user, society and enterprise.

Different approaches can be used to optimize this schedule. Those will be analyzed and take feedback data from a survey. This data will be inputted in a simulation in order to analyze the consequence of the different needs of the users. Furthermore in this report the infrastructure will be redesigned. The most important infrastructure alteration will be the placement of the bus depot. The simulation will also be used to validate the new dynamic scheduling system.

The validation will have with different requirement like waiting time and crowdiness but also with happiness. This is a design report for dynamic scheduling bus system, incorporating the needs of the User, Society and Enterprise.

Objectives

Objective	Short description
1.2.1	A potential maximum waiting time of 20% longer than it currently is.
1.2.2	An average decrease in travel time of 20% (not including during rush hours)
1.2.3	An increase in happiness of the travelers

1.2.1

When a bus skips a few bus stops, it will arrive sooner than expected at the next stops, which means that people might have to wait longer than usual for next bus if that one does not skip any. This will lead to potential longer waiting times. However, this also means that there are also potential shorter waiting times between busses, if the first bus does not skip any stops, but the second one does.

1.2.2

When there are stops that can be skipped, more efficient bus routes can be used to reduce the travel time. This will not be possible during rush hours, because the bus stops will be too crowded, which means that there will barely be any stops the bus can skip. In the following picture shows a fragment of the bus route between Eindhoven and Veldhoven.

1.2.3

Happiness will be “quantified” in the simulation, and the decrease in travel time should be weighing more heavily than the potential increase in waiting time, and perhaps the troubles caused by the use of the app to sign up for bus rides.

Requirements

Requirements	Short description
1.3.1	Automatically optimize the bus routes by skipping several stops according to check-in info from bus stops and web interface.
1.3.2	Arrange more buses to certain routes to make sure every passenger has a seat when passenger flow increases (determined by check-in info).
1.3.3	Keep the schedule static during heavy load hours. This schedule is predetermined by adapting from the traditional scheduling systems.
1.3.4	The scheduling system must be able to switch to manual control mode.
1.3.5	The scheduling system must be able to collect feedback data from user through various methods( web interface, phone apps, etc...).
1.3.6	The system must be able to track locations of each bus and this info will be provided to user through various ways ( sms notifications, web interface, mail, phone apps, etc…)

1.3.1

When there’s no one checked in in the coming bus stops, the scheduling system should try to search for a shortcut to skip them by taking current traffic loads into account. The system should only search within the available routes (without disturbing the neighborhoods) and send the new route to the bus.

1.3.2

The scheduling system will arrange more busses to certain routes to control the crowdiness. By crowdiness we mean outside the heavy load hours every passenger should have a seat.

1.3.3

The scheduling system should not optimize the routes dynamically during heavy load hour. When doing so the system won’t be conflict with other transport systems.

1.3.4

The scheduling system provides the interface to manually control the routes and schedule for special situations ( fire, natural disasters, etc…)

1.3.5

The scheduling system provides the interface for collecting user feedbacks like “too late” “too earlier and missed it” “5 min later than expected”. This data can be used to further optimize the system’s optimization algorithm.

1.3.6

The system will send notifications to user who has pre checked-in for route changing, reminds and other info. User will be able to choose the way of how this notification sent to them. User who have not pre checked-in can found these data on web interface/phone apps.

Proposed Plan of Action

As can be seen, we have already clarified the background, objectives, and the requirements regarding the autonomous bus scheduling system. Now, we will we explain how we are going to investigate whether such a system could replace the current scheduling system.

We have three main cases we should distinguish:

1. Will the ABSS decrease the average waiting and traveling time of the passengers?

2. Will the ABSS enhance the beneficial looking at the three USE-aspects?

3. Should autonomous busses replace the bus drivers?

In order to investigate whether or not the ABSS will decrease the average waiting and traveling time, we will create a simulation with as well as possible real life features. With real life features a Poisson Process for passenger arriving time for example is meant. We use Unity3D (Frank should update it) to make this simulation.

For the second and last case we will conduct a survey and distribute them among at least 70 bus passengers, the passengers are not entirely randomly selected, but are selected so that in each age and gender category we have about the same amount of people.

Besides the survey we will study research that already has been done in scientific journals or informative magazines, analyze those findings and apply them in our own investigation.

Past attempts

Time	Description	Author
1967	The scheduling problem has been formulated as a constrained optimization problem for frequency determination. The objective was to minimize the total travel time for a given fleet size constraint and a random search procedure was used for solution.	Lampkin, W. and Saalmans, P. D.
1972	A model was proposed to solve the optimization problem. This model searches for an optimum bus network by adjusting iteratively the frequencies and type of buses on each link to correspond to the link flow level, such that the service on some links is enhanced whilst on others it is depleted. The optimum situation is reached when no further change in link service levels is detected.	Rea, J. C.
1974	A system has been proposed that can determine optimum frequencies for a set of bus routes and fleet size, which could minimize the total travel time and discomfort (travelling without a seat) by a gradient method procedure.	Silman, L. A., Barzily,Z. and Passy, U.
1977	A mathematical programming algorithm of the compound minimization type for bus traffic model has been developed. The problem of optimal bus frequencies is solved by a gradient projection method.	Scheele, S.
1980	A detailed simulation model for studying the effect of frequencies on various route level and network level measures of effectiveness.	Dhingra, S. L.
1981	They developed a model that allocates the available buses between time period and between routes so as to maximize net social benefit subject to constraints on total subsidy, fleet size and level of vehicle loading.	Furth, P. G. and Wilson, N. M. H.
1982	Another model that recognizes passenger route choice behaviour and seeks to minimize a function of passenger wait time and bus crowding subject to constraints on number of buses available and the provision of enough capacity on each route.	Han, A. F and Wilson, N. M. H
1994	The model used the previous concepts of optimal vehicle size, frequency adjustment for co-ordinated routes, timed transfer and transit centers.	Shih, M. and Mahmassani, H. S.
2015	this report	Joey Verest

References

1.Lampkin, W. and Saalmans, P. D. (1967). The Design of Routes, Service Frequencies and Schedules for a Municipal Bus Undertaking: A Case Study, Operation Research Quarterly 18, pp 375 - 397.

2.Rea, J. C. (1972).Designing Urban Transit Systems: An Approach to the Route Technology Selection Problem, Highway Research Record 417, Highway Research Board, Washington, D. C., pp 48 - 58.

3.Silman, L. A., Barzily,Z. and Passy, U. (1974).Planning the Route System for Urban Buses, Computers and Operations Research, Vol. 1, pp 201 - 211.

4.Hsu, J. and Surti, V. H. (1977). Decomposition Approach to Bus Network Design, ASCE Journal of Transportation Engineering, Vol. 103, pp 447-459.

5.Scheele, S. (1977). A Mathematical Programming Algorithm for Optimal Bus Frequencies, Ph.D. thesis, Department of Mathematics, Linkoping University, Linkoping, Sweden

6.Dubois, D., Bell, G. and Llibre, M. (1979). A Set of Methods in Transportation Network Synthesis and Analysis, Journal of Operations Research Society, Vol. 30, No.9, pp 797-808.

7.Dhingra, S. L. (1980). Simulation of Routing and Scheduling of City Bus Transit Network, Ph.D. thesis, Department of Civil Engineering, lIT Kanpur, INDIA.

8.Furth, P. G. and Wilson, N. M. H.(1981). Setting Frequencies on B-us Routes: Theory and Practice, Transportation Research Record 818, Transportation Research Board, Washington, D. C., pp 1 - 7.

9.Han, A. F and Wilson, N. M. H (1982). The Allocation of Buses in Heavily Utilized Networks With Overlapping Routes, Transportation Research B, Vol. 16, No.3, pp 221 -232.

10.Baaj, M. H. (1990). The Transit Network Design Problem: An AI-Based Approach, Ph.D. thesis, Department of Civil Engineering, University of Texas, Austin, Texas.

11.Shih, M. and Mahmassani, H. S. (1994). A Design Methodology for Bus Transit Networks with Coordinated Operations, Research Report 60016-1,Center for Transportation Research, University of Texas at Austin, Austin, Texas.

12.Dashora, M. (1994). Development of an Expert System for Routing and Scheduling of Urban Bus Services, Ph.D. thesis, Department of Civil Engineering, lIT Bombay, INDIA.

13.“A survey of public opinion about autonomous and self-driving vehicles in the U.S., the U.K. and Australia”, Brandon Schoettle and Michael Sivak, July 2014

14.“Ethical Decision Making During Automated Vehicle Crashes” in Transportation Research Record: Journal of the Transportation Research Board, Noah J. Noah.

15. U.S. Census Bureau. Vehicles Involved in Crashes by Vehicle Type, Rollover Occurrence, and Crash Severity: 2009. Statistical Abstract of the United States. Publication Table 1107. U.S. Department of Commerce, 2012.

16. Office of Freight Management and Operations. Freight Facts and Figures 2011. Publication FHWA-HOP-12-002. FHWA, U.S. Department of Transportation, 2011.

17. NHTSA. Traffic Safety Facts 2009: A Compilation of Motor Vehicle Crash Data from the Fatality Analysis Reporting System and the General Estimates System. Publication DOT HS 811 402. U.S. Department of Transportation, 2009.