Research Article
Open Access

Investigation on Index of Accessibility to Public
Transportation Services for Developing Feeder Network

Majid Mahdinia

^{*}
Department of Management, Islamic Azad University of Babol, Iran

***Corresponding author:**Majid Mahdinia, Department of Management, Islamic Azad University of Babol, Iran. Tel: +989113000169; E-mail:

Received: July 13, 2018; Accepted: September 3, 2018; Published: November 29, 2018

**Citation:**Mahdinia M (2018) Investigation on Index of Accessibility to Public Transportation Services for Developing Feeder Network. Int J Fam Busi Manag 2(2): 1-7.

Abstract

Nowadays with developing and city growth, also city systems need
to be update and developing parallel to expand of cities. So it is wisely
that getting forward the network reforming and city systems with
needs and today’s conditions of society and city. Public transportation
system is one of the largest and the most complex city systems that has
very important rule on speediness of citizen transition; so reforming
and making changes for optimization the efficiency amount of system
is not easily possible.

In this thesis has been paid attention to introduce and determine the measure of accessibility indices affect to public transportation network with walking mode and present a method for developing feeder network of public transportation system. We calculated the coefficients of accessibility indices by Sampling Statistical in city of Tehran. In this method uses three algorithms for evaluating time and comforting applicants travel and also present and taking result a model for reforming location of public transportation stations and a model for designing feeder routes that with investigating and comparing work has done. In continue with segregate performing models and algorithms we have found the desirable results. The results of performing public transportation network evolution for subway-BRT network in four central areas of Tehran, the number of the least transfers on the shortest paths from a point to another point on network and also the number of the least transfers on paths between two points has presented. Performing place reform model of network stations for a virtual network, results appropriate covering of travel demand routes. The results of routes feeder designing model for area ten of Tehran that from accessibility level to system with slowly walking is four feeder routes with covering all elected stops for covering area. Also we perform developing feeder network method for virtual network that results are 76 percent increase in accessibility measure. At end also we perform said method for virtual network that result is 81 percent increase in accessibility measure to system in standard time and 16.5 percent decrease of summation traveling time. Keywords: Public transportation system; access to system; evaluation of network; transfer; travels time; locating; feeder routes;

In this thesis has been paid attention to introduce and determine the measure of accessibility indices affect to public transportation network with walking mode and present a method for developing feeder network of public transportation system. We calculated the coefficients of accessibility indices by Sampling Statistical in city of Tehran. In this method uses three algorithms for evaluating time and comforting applicants travel and also present and taking result a model for reforming location of public transportation stations and a model for designing feeder routes that with investigating and comparing work has done. In continue with segregate performing models and algorithms we have found the desirable results. The results of performing public transportation network evolution for subway-BRT network in four central areas of Tehran, the number of the least transfers on the shortest paths from a point to another point on network and also the number of the least transfers on paths between two points has presented. Performing place reform model of network stations for a virtual network, results appropriate covering of travel demand routes. The results of routes feeder designing model for area ten of Tehran that from accessibility level to system with slowly walking is four feeder routes with covering all elected stops for covering area. Also we perform developing feeder network method for virtual network that results are 76 percent increase in accessibility measure. At end also we perform said method for virtual network that result is 81 percent increase in accessibility measure to system in standard time and 16.5 percent decrease of summation traveling time. Keywords: Public transportation system; access to system; evaluation of network; transfer; travels time; locating; feeder routes;

Introduction

Shape of a city has regarding effect on lifestyle of inhabitant of
that city. For expanding downtowns need to have special strategy
which do not make quality of life in contrast of development.
Transportation programming is an important strategic theory.
Optimum usage of facilities and systems are one of the most up
to date cases in technology worlds which is used for less use of
energy and more saving of it and also wiled for fast reaction, saving
time and cost, diminish of contamination, spoilt of nature and echo
systems for fauna and Fiona. Hence, organs and scientific centers
are attempted to make these amounts optimum and making
possible damages minimum. One of the most important problems
of human from the first to now is increasing of transportation speed
from a point to another point which is solved by daily progress of
science and industry fortunately; on the other hand technology can
control different types of business and education travels which are
made by today needs, but for more retaining of these problems face
to use of different kinds of technologies and sciences. Science of
optimizing and fast type of algorithms can be more helpful in this
field.

Transportation optimizing case includes different parts such as management and programming, designing and modeling which is either includes designing transportation network, facility location in transportation network and itinerary of services. For more domination on systems should find different aspects and features and investigate on variety of conditions. Also investigation and studying of other countries used city systems can help either.

Types of modalities for access to the public transportation system

Methods of public transportation systems divided into fields of walking mode and non- walking mode which are indicated below:

• Non-walking mode

• Cycling mode

• Private or taxi mode

• Feeder mode service

• Other non-walking mode (motor cycle, skates and etc.)

• Walking mode

Below chart indicates amount of different modes for accessing and exiting from public transportation in two cities and one country which walking mode and bus mode have the most amount of using (Figure 1).

Transportation optimizing case includes different parts such as management and programming, designing and modeling which is either includes designing transportation network, facility location in transportation network and itinerary of services. For more domination on systems should find different aspects and features and investigate on variety of conditions. Also investigation and studying of other countries used city systems can help either.

Types of modalities for access to the public transportation system

Methods of public transportation systems divided into fields of walking mode and non- walking mode which are indicated below:

• Non-walking mode

• Cycling mode

• Private or taxi mode

• Feeder mode service

• Other non-walking mode (motor cycle, skates and etc.)

• Walking mode

Below chart indicates amount of different modes for accessing and exiting from public transportation in two cities and one country which walking mode and bus mode have the most amount of using (Figure 1).

**Figure 1:**Amount of using modes for accessing and exiting

Feeder services mode

Feeder services, endemic services or feeder services are known
as the most important non- walking modes. These services are as
feeder for users who are inhabitant in weak public transportation
access and also are provided in high rise, schools and official
institutions. Instruction of this service starts from residential area
to the nearest subway or bus rapid transit stop and then back to
start nude. These services are available to transit more numbers
of people in compare with taxi and also they have facilities of
travel comforting such as BRT. Another advantage of this mode is
providing more selection for users and attracting passengers with
short travel.

Basic definitions

Definition of flow is each path of bus or subway from start to end in network
(being attention in this chapter, path from start to end is separated; it means each flow is on one way).

Set of main nudes of network (stops) N and number of member.

Set of nudes which appeared block centers Z and number of members ${N}_{0}$ is. Also between numbers of this set in overall network there is not any arc.

Sets of ${N}_{k}$ which are include nudes from N is distance (time) of availability from center of block K to these nudes (stops) is less than or equal with standard time of availability. Also ${N}_{\mathrm{max}}$ is defined maximum in set of {|N_k |:k∈Z}. Overall network$\overline{G}=(N{{\displaystyle \cup}}^{\text{}}Z,B)$ of nudes include all nudes of stops and nudes of block centers and set of arcs include all arcs of main network extra all defined arcs between center of blocks and stop sets (e.g. if $i\in N$ or $i\in N$ and $j\in Z$ access time be less or equal with standard time for i to j, then $(j,i)\in B$ . Sets of travel in network is (each travel is considered for a person with determination of start and end) Q and its members ${Q}_{0}$ .

Set of all flow (roots) is R and its members ${R}_{0}$

In this manuscript two ways for estimating distance between stops are indicated, the initial way is for estimation of distance between stops by population density and the latter way is based on well-known way in England.

Set of main nudes of network (stops) N and number of member.

Set of nudes which appeared block centers Z and number of members ${N}_{0}$ is. Also between numbers of this set in overall network there is not any arc.

Sets of ${N}_{k}$ which are include nudes from N is distance (time) of availability from center of block K to these nudes (stops) is less than or equal with standard time of availability. Also ${N}_{\mathrm{max}}$ is defined maximum in set of {|N_k |:k∈Z}. Overall network$\overline{G}=(N{{\displaystyle \cup}}^{\text{}}Z,B)$ of nudes include all nudes of stops and nudes of block centers and set of arcs include all arcs of main network extra all defined arcs between center of blocks and stop sets (e.g. if $i\in N$ or $i\in N$ and $j\in Z$ access time be less or equal with standard time for i to j, then $(j,i)\in B$ . Sets of travel in network is (each travel is considered for a person with determination of start and end) Q and its members ${Q}_{0}$ .

Set of all flow (roots) is R and its members ${R}_{0}$

**Matrix of flows:**matrix A, which is sorted flow from each line. If numbers of nudes be less than number of inline of each line we make other extra inline of that line as zero. In addition numbers of inline in each line in flow matrix are equal with numbers of flow nude which has the most numbers of nude.**Generic Network Proximity Matrix:**cost arc matrix C, which is defined by each incline of this matrix for time of access from one nude of generic network to another nude (for mode of vehicle and mode of walking has calculated separately base on speed and distance).In this manuscript two ways for estimating distance between stops are indicated, the initial way is for estimation of distance between stops by population density and the latter way is based on well-known way in England.

Literacy of research

He has worked on number of optimum bus stops in a network;
in this research was used from strategic way for measuring
maximum degree and non-efficient of it for covering bus stop for
public transportation system by bus stop for complete coverage of
set (LSCP1). Goal of LSCP is making number of bus stops minimum
for providing complete coverage of access in investigated sphere.
There are determining measures for standard distance of
accessibility in this model and also is used from circles with stop
flow and fix radius for accessing to system [1].

They are indicated two ways for optimum locating between stops on flows for balancing the accessibility. Models are written based on p-Median and in models making accessibility maximum is regarded. First model is designed for locating stops from network candidates on flow that each candidate has chosen for being as stop for two-way of flow, but the second model has used in special condition and locating is done for two-way flow [2].

They have indicated an algorithm for developing accessibility. The objective function of minimizing total cost (total cost of producer and user) with optimizing number and place of stops and regarding to restriction of time value of users is regarded. Producer cost is equal with accessibility to system, waiting time divide on vehicle cost which sum of user cost and producer cost is depended on total cost function [3].

He is mentioned that the best locating of stops is not as appear as it seems, because each of two reasons below could be discussed [4].

1. Many of stops are lucrative because accessibility is high on them.

2. Each stop can increase time of transportation, because each extra stop can increase time of average speed of bus.

One optimum solution can balance two goals by responding to restrictions, but weight or valuation to different and non-unitary goals can be hard.

They are indicated two ways for optimum locating between stops on flows for balancing the accessibility. Models are written based on p-Median and in models making accessibility maximum is regarded. First model is designed for locating stops from network candidates on flow that each candidate has chosen for being as stop for two-way of flow, but the second model has used in special condition and locating is done for two-way flow [2].

They have indicated an algorithm for developing accessibility. The objective function of minimizing total cost (total cost of producer and user) with optimizing number and place of stops and regarding to restriction of time value of users is regarded. Producer cost is equal with accessibility to system, waiting time divide on vehicle cost which sum of user cost and producer cost is depended on total cost function [3].

He is mentioned that the best locating of stops is not as appear as it seems, because each of two reasons below could be discussed [4].

1. Many of stops are lucrative because accessibility is high on them.

2. Each stop can increase time of transportation, because each extra stop can increase time of average speed of bus.

One optimum solution can balance two goals by responding to restrictions, but weight or valuation to different and non-unitary goals can be hard.

Location Set Covering Problem

Retnani (2008) has presented a way for locating new stops in
existed public transportation. In this research there are two kinds
of goals. First, minimizing number of stops with coverage demand
nudes with standard distance condition. Second, fixed number
of stops and then minimizing sum of accessibility distance from
demand nude. Thereby, there are two separated issues which have
their own responses and have wide selection area.

He has presented a model for locating stops by weighting to minimizing goals in access point and number of stops. Also in this model, there is penalty for weak accessing level which can achieve to high amount of accessibility for nudes with increasing penalty which make possibility for increasing number of stops [5].

He has presented two steps model for optimizing bus stops. At the first step bus is place throughout the public transportation in microscopic scale while in the same time cost of network is minimized. In the second step, by using microscopic solution which was achieved, stops are locating in microscopic or minor scale in special path of city [6].

He was worked on presenting developing of locating optimum model. Model uses from a continuous approximation and multicycle corridor demand for locating stops, minimizing operating costs and the total cost of passengers. The simultaneous model optimizes stops densities and hubs between sequential buses [7].

He has presented a model for locating stops by weighting to minimizing goals in access point and number of stops. Also in this model, there is penalty for weak accessing level which can achieve to high amount of accessibility for nudes with increasing penalty which make possibility for increasing number of stops [5].

He has presented two steps model for optimizing bus stops. At the first step bus is place throughout the public transportation in microscopic scale while in the same time cost of network is minimized. In the second step, by using microscopic solution which was achieved, stops are locating in microscopic or minor scale in special path of city [6].

He was worked on presenting developing of locating optimum model. Model uses from a continuous approximation and multicycle corridor demand for locating stops, minimizing operating costs and the total cost of passengers. The simultaneous model optimizes stops densities and hubs between sequential buses [7].

Methodology

In many of city travels, users for arrival to destination need
to exchange public travels, thereby can extract precise output by
adding new variable R. R is equal with multiplication of exchange
travel times average in number of exchanges travel average (for
instance if time average of exchange travel be 80 second and
two separate travel have 2 and 0 exchange travel amount of R for
these two travels is equal with $\frac{0+2}{2}\times 80$
), thus by regarding to R equation1 convert to below equation:

$$T=2.\frac{X}{F}+\frac{L}{D}.S+\frac{L}{D}.B+\frac{L}{D}.\frac{D-A}{V}+R$$

Main model of this difficult is as

$$T=2.\frac{X}{F}+\frac{L}{D}.S+\frac{L}{D}.B+\frac{L}{D}.\frac{D-A}{V}+R$$

Main model of this difficult is as

Data and decision making of this difficult are defined as:

${a}_{i}$ : Amount of demand per weight

${d}_{ij}$ : Distance of central weight of I from candidate j

P: Number of selected nudes from candidate nudes for making stop Decision variables:

${z}_{ij}$ : Coverage variable of me by candidate j

${x}_{j}$ : Binary variable for being stop or not candidate j

${a}_{i}$ : Amount of demand per weight

${d}_{ij}$ : Distance of central weight of I from candidate j

P: Number of selected nudes from candidate nudes for making stop Decision variables:

${z}_{ij}$ : Coverage variable of me by candidate j

${x}_{j}$ : Binary variable for being stop or not candidate j

Definition of Goal Function and Restriction of Model

Goal function of model is sum of accessibility distance. Restriction number one: each nude allocate demand to only one convenient nude. Restriction number two: number of candidate for being stop are appeared by number of p. restriction number three: selected candidate nude insure allocated demand nude for being stop.

Data

${c}_{i}$
: Cost of selecting nude i for being stop (which can be cost of making stop in nude i)

${d}_{ij}$ : Distance of accessibility of demand nude of j to candidate nude of i

${d}_{\mathrm{max}}$ : Maximum allowed distance for accessing from a point to stop

$DN$ : Sum of demand nude

$CN$ : Sum of candidate nudes for being stop

${d}_{ij}$ : Distance of accessibility of demand nude of j to candidate nude of i

${d}_{\mathrm{max}}$ : Maximum allowed distance for accessing from a point to stop

$DN$ : Sum of demand nude

$CN$ : Sum of candidate nudes for being stop

Decision variables

${x}_{i}$
: Binary variable foe selecting or non-selecting candidate i for being as stop

${y}_{ij}$ : Determination binary variable for covering or non-covering of j demand nude by candidate nude

${y}_{ij}$ : Determination binary variable for covering or non-covering of j demand nude by candidate nude

Definition of goal function and restriction of model

Goal function is minimizing sum of costs. Restriction number
one: allocating one demand nude only necessary one candidate
nude. Restriction number two expressed that if one demand nude
allocated to candidate nude in allowed distance, that candidate
nude should be chosen as stop. Restriction number three and four
also express being binary of variables.

In some of transportation networks, parts of route went and back path have distance from each other or it cannot be possible to make both route and back path into same flow. In these kinds of conditions should use from two ways location stops.

At first, define ${M}_{j}$ as coincident with j candidate due to against of direction from j then ${\overrightarrow{N}}_{r}$ which are as candidate nudes of r flow in one way and sum of candidate nude in another direction of r flow are introduced. Collection of these sets constitute set of all candidate nudes ${\stackrel{-}{N}}_{r}=\left\{j\right|j\in {\overrightarrow{N}}_{r}{{\displaystyle \cap}}^{\text{}}\left|{M}_{j}\right|\ne \varphi \}$ . Is defined as sum of which are in ${\overrightarrow{N}}_{r}$ .

Now by expressed changes in below steps in goal function and restriction of model find new model.

First step: ∑_(i=1)^I▒〖(1-∑_(j=1)^N▒z_ij )P 〗is found from minimized goal function from total time

In some of transportation networks, parts of route went and back path have distance from each other or it cannot be possible to make both route and back path into same flow. In these kinds of conditions should use from two ways location stops.

At first, define ${M}_{j}$ as coincident with j candidate due to against of direction from j then ${\overrightarrow{N}}_{r}$ which are as candidate nudes of r flow in one way and sum of candidate nude in another direction of r flow are introduced. Collection of these sets constitute set of all candidate nudes ${\stackrel{-}{N}}_{r}=\left\{j\right|j\in {\overrightarrow{N}}_{r}{{\displaystyle \cap}}^{\text{}}\left|{M}_{j}\right|\ne \varphi \}$ . Is defined as sum of which are in ${\overrightarrow{N}}_{r}$ .

Now by expressed changes in below steps in goal function and restriction of model find new model.

First step: ∑_(i=1)^I▒〖(1-∑_(j=1)^N▒z_ij )P 〗is found from minimized goal function from total time

*Z*removed and replace below expression instead of_{timeis}*Z*:_{timeis}Second step: remove restriction two and replace two below
restrictions instead of it:

Third step: adding this new restriction to model:

First step obliged each demand nude allocated at least to two
candidate nudes. Presented restrictions in second step ensue
to each demand nude be allocated to one stop in each direction.
Presented restriction in third step caused that if one candidate
nude selected as stop, candidate nude also selected in another
direction as stop.

If in city network use from accessibility index for measuring distance or time, some of route way and back pass will be different together. Locating model for this condition can be achieved by mentioned locating model. Expressed changes in below steps and restrictions were defined can make new model.

First define new variables. ${z}_{ij}$ is coverage variable for demand nude i by candidate j for route way; $z{\text{'}}_{ij}$ is coverage demand nude variable i by candidate j for back path; ${d}_{ij}$ is distance of accessing from demand nude i to candidate j;$d{\text{'}}_{ij}$ is accessing distance from demand nude i to candidatej; ${a}_{i}$ is amount of producing travel in demand nude of j;$a{\text{'}}_{i}$ Is amount of attracting travel in demand nude i; ${\theta}_{ij}$ is average number of traveling from nude j and j nude for accessing of user to demand nude i and exit user from demand nude i ${V}_{ij}$ and is average time of traveling from nude j to j for travel applicants to demand nude of i and exit applicants from demand nude i. Now with these expressed changes in goal function and restrictions, new model can be achieved:

First step: goal functions of

If in city network use from accessibility index for measuring distance or time, some of route way and back pass will be different together. Locating model for this condition can be achieved by mentioned locating model. Expressed changes in below steps and restrictions were defined can make new model.

First define new variables. ${z}_{ij}$ is coverage variable for demand nude i by candidate j for route way; $z{\text{'}}_{ij}$ is coverage demand nude variable i by candidate j for back path; ${d}_{ij}$ is distance of accessing from demand nude i to candidate j;$d{\text{'}}_{ij}$ is accessing distance from demand nude i to candidatej; ${a}_{i}$ is amount of producing travel in demand nude of j;$a{\text{'}}_{i}$ Is amount of attracting travel in demand nude i; ${\theta}_{ij}$ is average number of traveling from nude j and j nude for accessing of user to demand nude i and exit user from demand nude i ${V}_{ij}$ and is average time of traveling from nude j to j for travel applicants to demand nude of i and exit applicants from demand nude i. Now with these expressed changes in goal function and restrictions, new model can be achieved:

First step: goal functions of

*Z_*change as:_{time}and Z__{cost}Second step: remove restriction 8 and replace below restriction:

Third step: adding these restrictions to model:

In first step times and costs of attracting demand nudes in goal function is placed in goal function, also should regarded to non-waiting time for applicants who want demand nude of last stop. In second step revise determined minimum abundance in each flow for passengers with start demand nude and with end demand nude. In third step also are introduced restrictions of determined amount of variable $z{\text{'}}_{ij}$
(allocated variable of a nude to candidate nude foe accessing to one demand candidate nude).

Response of Difficult by Using Genetic Algorithm: Executed by MATLAB

Because of NP-Hard of difficult, investigated locating for huge data should be done by initiative algorithm. For this reason genetic algorithm was used for multi-time response of difficult. In used algorithm each gene is named by a candidate. If amount of named candidate be equal to one, corresponding candidate with gene is chosen as stop and if named amount be equal to zero candidate does not choose as stop. Each chromosome includes 29 genes which are as the number of each candidate stop and 4 stops are fixed in difficult which constitute 33 nudes totally. Stake of cease for algorithm is producing 1000 generations which are included 220 chromosome, it means 220000 response for difficult will be investigated. First generation includes 15 parents and in each step 15 parents will choose from the last generation of Crossover chromosome. Possibility of selection a chromosome for each generation for producing next generation will determine by amount of fitness.

Crossover is done by binary method and mutation also is done by decreasing of random digit from 0.07 for each chromosome as changing amount of a random gene from that chromosome. First amount of fitness for each chromosome is equal to zero and for each non- approval of chromosome in a restriction, amount of one will add to fitness amount of that chromosome.

After executing algorithm in 43 minutes, significant response (fitness=0) with the best amount of goal function between responses (chromosome) is caught.

After executing algorithm in 43 minutes, significant response (fitness=0) with the best amount of goal function between responses (chromosome) is caught.

Some of Designing Feeder Line Models

Public transportation system or network line of crowded vehicle (such as subway or BRT) which are used or named as system or main network. Demand stops also defined as pinpoints stops in under study sphere (sphere with low amount of accessibility to system). Models of this section are looking for wide path for feeder passed services from demand stops and want to find other paths such as frequency of services in each line.

Resulted Model Based on Studied Model

Based on investigation and comparison of under studying models, we present more precise model with three separated goal model as sum of travel time function, total cost function and total accessibility value function. Accessibility value calculate base on accessing to one main network stop for applicants of demand stop. In below model, restriction of response to amount of demand services by decreasing accessibility time to main network stops, restriction of responding to maximum number of main network stop, coefficient restriction of feeder line and closed of feeder line is wield. Amount of average demand per hour and length of path between demand stops as entrance and frequently of service between lines per hour and path of each line are as output of model.

Number of lines has selected before and valuations${v}_{i}$ also can achieve by other methods.

Number of lines has selected before and valuations${v}_{i}$ also can achieve by other methods.

Conclusion

In this research initially worked on investigation on
accessibility index to public transportation systems and presenting
way to calculate accessibility time and presented the way for
determining accessibility level, then indicate algorithms for
evaluating public transportation network based on accessibility
time, time of travel and number of travel replacement; In the
following, presented a model for locating and revising place of
public transportation network; Finally indicated a model for
designing feeder lines in low amount of accessibility sphere of
public transportation.

Mentioned steps should be done for optimizing public transportation network. The most important processes which are considered in this corrective research are:

Accessibility of people to systems, time of people accessibility to system and time of exit, waiting time of applicants in system stops for accessing to system, time of vehicle or time of traveling by vehicle, number of traveling replacement for traveling of applicants, cost of applicants and operator cost.

Each mentioned item is very important and achieving of each one value is not possible as easy as it seems. Time is a runner who never look at his back and exchange is not something that could catch easy, thus time and cost which we paid for research and study and in the following present the method to correcting structure, across sum of these time and extra cost which are paid because of inefficiency or weak efficiency of social system such as public transportation system is very poor, so high amount of attempt for presenting more precise and profitable ways can help society to propel forward incredibly.

Mentioned steps should be done for optimizing public transportation network. The most important processes which are considered in this corrective research are:

Accessibility of people to systems, time of people accessibility to system and time of exit, waiting time of applicants in system stops for accessing to system, time of vehicle or time of traveling by vehicle, number of traveling replacement for traveling of applicants, cost of applicants and operator cost.

Each mentioned item is very important and achieving of each one value is not possible as easy as it seems. Time is a runner who never look at his back and exchange is not something that could catch easy, thus time and cost which we paid for research and study and in the following present the method to correcting structure, across sum of these time and extra cost which are paid because of inefficiency or weak efficiency of social system such as public transportation system is very poor, so high amount of attempt for presenting more precise and profitable ways can help society to propel forward incredibly.

ReferencesTop

- Laporte G. The vehicle Routing problem: An overview of Exact & Approximate Algorithm, European Journal of Operational Research. 1992;59(3):345-358.
- Ceder A. Public Transit Planning and Operation: Theory, modeling and practice
*,*First edition. Elsevier. 2007. - Ahuja RK, Magnanti TL, Orlin JB. Network Flows: Theory, Algorithms and Applications, PRENTICE HALL. 1993.
- Nielsen G. HiTrans Best practice guide 2: Public transport – Planning the networks, HiTrans, c/o Rogal and County Council. 2005.
- ECMT group;
*improving access to public transport*, OECD Publications, 2004. - TFL group; measuring public transport accessibility levels.
- Wibowo SS, Olszewski P. Modeling walking accessibility to public transport terminals: case study of Singapore mass rapid transit, Journal of the Eastern Asia Society for Transportation Studies. 2005;6:147-156.
- Cervero R. Walk-and-Ride: Factors Influencing Pedestrian Access to Transit, Journal of Public Transportation, Transportation Research Board. 2001;3(4):1-23.
- Murray AT, Davis R, Stimson RJ, Ferreira L. Public Transportation Access, Transportation Research Part D: Transport and Environment. 1998;3(5):319-328.
- Murray AT. Strategic analysis of public transport coverage, Socio-Economic Planning Sciences, Elsevier. 2001;35(3):175-188.
- Murray AT, Wu X. Accessibility tradeoffs in public transit planning, Journal of Geographical Systems, Springer. 2003;5(1):93-107 .
- Chien S. Qin Z. Optimization of Bus Stop Locations for Improving Transit Accessibility, Transportation Planning and Technology, Taylor and Francis group. 2004;27(3):211-227.
- Schöbel A. Locating Stops Along Bus or Railway Lines-A Bicriteria Problem, Annals of Operations Research, Springer, 2005;136(1): 211-227.
- Al Mamun MS, Lownes NE. A Composite Index of Public Transit Accessibility, Journal of Public Transportation. 2001;14:(2):69-87.
- Scheurer J. Sergio Porta. Centrality and Connectivity in Public Transport Networks and their Significance for Transport Sustainability in Cities, Presented at: World Planning Schools Congress. 2006.
- Hassan Ziari, Keymanesh MR, Mohammad M Khabiri. Locating Stations Of Public Transportation Vehicles For Improving Transit Accessibility, Transport. 2007;22(2):99–104.
- Jackson LE, Rouskas GN, Stallmann MF. The directional p-median problem: Definition, complexity and algorithms, European Journal of Operational Research, Elsevier. 2007;179(3):1097–1108.
- Fletterman M. Designing Multimodal Public Transport Networks Using Met heuristics,National University of Singapore, Thesis, 2008.
- Groß DR, HorstW. Hamacher, Simone Horn, Anita Schöbel. Stop location design in public transportation networks: covering and accessibility objectives, TOP. 2009;17(2):335-346.
- Moura JL, Alonso B, Ibeas A, Ruisánchez FJ. A Two-Stage Urban Bus Stop Location Model, Networks and Spatial Economics, Springer. 2012;12(3):403-420.
- Medina M, Giesen R, Muñoz JC. A Model for the Optimal Location of Bus Stops And Its 2 Application to A Public Transport Corridor In Santiago, 92nd Annual Meeting of the Transportation Research Board, Washington D.C. 2013;44
- Chien SJ. Optimization of Headway, Vehicle Size and Route Choice for Minimum Cost Feeder Service, Transportation Planning and Technology, Taylor and Francis group. 2005;28(5):359-380 .
- Chandran B, Raghavan S. Modeling and Solving the Capacitated Vehicle Routing Problem on Trees, Operations Research/Computer Science Interfaces, Springer. 2008;43:239-261.
- Hu Y, Zhang Q, Wang W. A Model Layout Region Optimization for Feeder Buses of Rail Transit, 8th International Conference on Traffic and Transportation Studies Changsha. 2012;43:1–3.
- Martínez LM, Eiró T. An optimization procedure to design a Minibus feeder service: an application to the Sintra rail line, 15thmeeting of the EURO Working Group on Transportation. 2012;54:525-536.
- Sze Nee K. Applying Metaheuristics to Feeder Bus Network Design Problem, National University of Singapore, Thesis. 2003.
- Jerby S, Ceder A. Optimal Routing Design for Shuttle Bus Service, Journal of the Transportation Research Board, No. 1971, Transportation Research Board of the NationalAcademies. 2006;14-22.
- Cervero R, Caldwell B, Cuellar J. Bike-and-Ride: Build It and They Will Come, Journal of Public Transportation, NCTR, forthcoming, 2013;16(4).
- Shrivastava P, O’Mahony M. Design of Feeder Route Network Using Combined Genetic Algorithm and Specialized Repair Heuristic, Journal of Public Transportation. 2007;10(2)109-133.
- AIMMS Community; AIMMS Modeling Guide - Integer Programming.
- WesteinITE/ Aspelin, Karen; Establishing Pedestrian Walking Speeds.
- University of Missouri; Lecture np.
- Reese J. Methods for Solving the p-Median Problem: An Annotated Bibliography, Networks. 2006;48(3):125-142.
- Mladenovic N, Brimberg J, Hansen P, Moreno Perez JA. The p-median problem: A survey of metaheuristic approache, European Journal of Operational Research. 2007;179(3):927–939 .
- Melanie M. An Introduction to Genetic Algorithms, fifth printing, MIT Press. 1999.
- Jiaqing W, Rui S, Wangtu X, Liu Y. Transit Network Optimization for Feeder Bus of BRT Based on Genetic Algorithm, Digital Manufacturing and Automation (ICDMA), Fourth International Conference on Digital Manufacturing & Automation, 2013;1627-1630.
- Yeun LC, Ismail WR, Omar K, Zirour M. Vehicle Routing Problem: Models and Solutions, Journal of Quality Measurement and Analysis, University Kebangsaan Malaysia. 2008;4(1):205-218.
- Ehrgott M. Multicriteria Optimization, Second edition. 2005.
- Meyer MD, Miller EJ. Urban Transportation Planning, Second Edition, McGraw-Hill International Edition, Singapore. 2001.
- Mitchell CGB, Stokes RGF. Walking as a Mode Transport, TRRL Laboratory Report 1064, Transport and Road Research Laboratory, Department of the Environmental, Department of Transport. 1982.
- Stringham M. Travel Behavior Associated with Land Uses Adjacent to Rapid Transit Stations, ITE Journal, 1982;52(4):16-18.
- Loutzenheiser DR. Pedestrian Access to Transit, Model of Walk Trip and their Design and Urban Form Determinants around Bay Area Rapid Transit Stations, Transportation Research Record No 1604, Transportation Research Board, National Research Council. 1997;40-49.
- Polzin SE, Chu X, Rey JR. Density and Captivity in Public Transit Success, Observation from the 1995 Nationwide Personal Transportation Study, Transportation Research RecordNo 1735, Transportation Research Board, National Research Council. 2000;10-18.
- Ryus P, Ausman J, Teaf D, Cooper M, Knoblauch M. Development of Florida’s transit level-of-service indicator, Transportation Research Record. 2000;1731:123-129.
- Cooper S. Measuring public transport accessibility levels: Sub matter 5b parking strategy, transport for London. 2003.
- Gent C, Symonds G. Advances in public transport accessibility assessments for development control – A proposed methodology, PTRC Annual Transport Practitioners’ Meeting, UK. 2005.
- Bhat CR, Bricka S, La Mondia J, Kapur A, Guo JY, Sen S. Metropolitan Area Transit Accessibility Analysis Tool, University of Texas, Austin; Texas Department of Transportation. TxDOT Project 0-5178-P3. 2006.
- Burke M, Brown AL. Distances people walk for transport. Road Transport Res. 2007;16(3):16–28.
- Curtis C, Scheurer J. Planning for sustainable accessibility: developing tools to aid discussion and decision-making, Progress in Planning. 2010;74(2):53–106.
- Ruff N, Schöbel A. Set covering problems with almost consecutive ones property, Discrete Optimization. 2004;1(2):215-228.
- Mecke S, Wagner D. Solving geometric covering problems by data reduction, Proceedings of European symposium on algorithms (ESA). 2004;60–771.
- Schöbel A, Hamacher HW, Liebers A, Wagner D. The continuous stop location problem in public, transportation networks. Asia-Pac J Oper Res. 2009;26(1):13-30.
- Hamacher HW, LiebersA, Schöbel A, Wagner D, Wagner F. Locating new stops in a railway network. ENTCS. 2001;50(1):1–11.
- Kranakis E, Penna P, Schlude K, Taylor DS, Widmayer P. Improving customer proximity to railway stations, In: Proceedings of the 5th conference on algorithms and complexity. Lecture notes in computer science. 2003;2653:264-276.
- Schöbel A. Locating stops along bus or railway lines-a bicriteria problem, Ann Oper Res. 2005;136(1):211–227.
- Mammana MF, Mecke S, Wagner D. The station location problem on two intersecting lines, ENTCS. 2004;92:52–64.
- Ibeas A, Dell’Olio L, Alonso B, Sáinz O. Optimizing bus stop spacing in Urban Areas, Transp Res. 2010;46(3):446–458.
- Vuchic VR. Urban Transit: Operations planning and economics, Hoboken, New Jersey: John Wiley&Son. 2005.
- Kuan SN, Ong HL, Ng KM. Solving feeder bus network design problem by genetic algorithms and ant colony optimization, Advances in Engineering Software. 2006;37(6):351–359.
- Yang S, Rui S, Shiwei H. Feeder bus network design under elastic demand, Journal of Jilin University (Engineering and Technology Edition). 2011;41(2):349-354.
- Chien S, Yang Z. Optimal feeder bus routes on irregular street network, Journal of Advanced Transportation. 2000;34(2):213-248.
- Jerby S, Ceder A. Optimal routing design for shuttle bus service, Transportation Research Record. 2007;1971:14-22.

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