KTH-Texxi Project 2010


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KTH-Texxi Project 2010

  1. 1. AG2421 – A GIS Project Geoinformatics, KTH, Period 2, 2010 Gyözö Gidofalvi T.A. Jan Haas Demand-Responsive Transit (DRT) Service in the Stockholm Area Group 1 Adeel Anwar, Alexander Jacob, Mahnaz Narooie, Ehsan Saqib, Annmari Skrifvare, li e oglio
  2. 2. Demand responsive Transit Service in the Stockholm Area 2 | P a g e Table of Contents Chapter 1 Introduction Chapter 2 Study Area and Data Description Chapter 3 Methodology Chapter 4 Results Chapter 5 Discussion Chapter 6 Conclusion References Appendix
  3. 3. Demand responsive Transit Service in the Stockholm Area 3 | P a g e Chapter 1 Introduction Demand Responsive Transport, known also as Demand-Responsive Transit (DRT), is an advance form of public transport, which is characterized by being user-oriented and flexible in routing and schedule. The service is normally run by public transport society / local transit authority (or municipalities) or co-founded by the public sector. DRT is used to achieve the need of transport in scattered-low density areas whit low passengers demand, where scheduled bus lines are hence not feasible (EU report, 2002). The system uses small or medium vehicles, operating in share-ride mode, in which pick-up and drop-off locations are optimized on passenger needs. For the GIS project the feasibility for starting a DRT service in the Stockholm area has been analyzed, based on the TEXXI concept (The Transit Exchange for the XXIst Century / The Shared Taxi you Text). The main goal is to support the implementation of such a service with a tool for finding the best areas for a pilot project. The presentation layer of this tool is an interactive web frontend, which is connected to a spatial database enriched by a lot of self written functions to serve the special needs of this project.
  4. 4. Demand responsive Transit Service in the Stockholm Area 4 | P a g e Chapter 2 Study Area and Data Description 2.1 Study Area Stockholm County consists of 26 municipalities and covers an area of 6519 km2. In 2008 the county had 1 949 516 inhabitants. This makes it the most densely populated area in Sweden. (Nationalencyklopedin) Public transport in Stockholm includes subway, commuters train, light rail and buses covering large areas of the county from Bålsta and Märsta in the north to Södertälje and Nynäshamn in the south. (Storstockholms Lokaltraffik) Figure 1 Study area (map from Eniro Dec 2010)
  5. 5. Demand responsive Transit Service in the Stockholm Area 5 | P a g e 2.2 Data description 2.2.1 Election statistics National electoral data from 2010 was provided. Administrative borders of different scale were given in vector shape files, in SWEFER99. Excel files with the corresponding attribute data were also given. We decided that it was hard to make any well founded assumptions about travel behavior and political party and did not use this data. 2.2.2 Extended mosaic Mosaic data of geodemographics were provides in vector shape files on two different levels, postal code and mosaic area. The attributes of the geodemographics were age, education, type of economic or industrial sector, healthcare, income, housing, cars and day and night population etc. Year of acquisition is unknown. 2.2.3 Points of interests Points of interest were provided by Dong Fang in point data format. The points included amusement, restaurants and bus stops. Finally we did not use the data. 2.2.4 Road network Road network data and land cover data were provided in vector shape files in SWEFER99 TM, RH 2000. The data includes road, railroad, built up area, water areas etc covering Stockholm County. Year of acquisition is unknown. 2.2.5 Simulated travel demand Simulated demand was given in CSV matrix files either car trips or non car trips. The files contain information about origin and destination of trips for 24 hour period and cover purpose and mode. Peak or off-peak hour information was also given. Corresponding geographical information was made available in shape files. 2.2.6 Taxi positions & journeys The taxi data set was provided directly into a database. It covered 1500 taxis and their GPS position during certain hours of the day. Since taxi data neither covered our area spatially (all trip zones) nor in time (24 h period), we chose not to use it.
  6. 6. Demand responsive Transit Service in the Stockholm Area 6 | P a g e Chapter 3 Methodology 3.1. Literature review The DRT run by TEXXI is oriented towards market results and the aim is not the one that characterizes the service provided by local transport authority. In order to investigate the opportunity/feasibility for this service in the Stockholm area, a literature review has been carried out in order to identify the potential costumers. Since the service is run y p iv e comp ny, he po en i l co ume ’ indic o h ve een based on DRT studies, combined with car sharing one. DRT literature enlighten the concept of the service and the indicators used in different case studies, which are useful to indentify the system profile, but are mainly oriented towards the public offer of transport means. Therefore some indicators (age, car ownership, income) have been checked and combined with the one coming from the car-sharing/pooling field, based on similar concept of sharing ride, but better oriented towards profitability. Acknowledgements Valmyndigheten – Electoral data 2010 Experian - Mosaic data openstreetmap.org - Points of interests Lantmäteriet Metria – Road network 3.2 Data preparation 3.2.1 Cleanup & Selection All data needed to be checked for its usability and validity. To also increase the clarity in the data, a process of selection of relevant data needed to be performed. The shape files containing the road network data for example contain a lot of irrelevant data for us who just want to use it as a background for our images. The O/D matrices forming the simulated travel demand covered a larger area than the corresponding shape file. For us only data that is spatially referencable was of interest. Due to that fact we removed those pairs of origin and destination which were not referencable. We l o couldn’ find in e e ing co el ion e ween vo ing eh vio nd he u ge of DRT-services in our literature review and discarding this data set completely. Finally we matched the factors found from the literature review against our demographic data source namely the mosaic data and selected only those which were of relevance for the project.
  7. 7. Demand responsive Transit Service in the Stockholm Area 7 | P a g e 3.2.2 Fusion All of our analysis is centered at trip zones level. The demographic data such as population, age level, etc was not provided at that level so a matching/fusion was required to have the data at the trip zone level. The data which was available to us was in two forms: value data and percentage data. By value data we mean that we were provided a whole value such as total population in a certain area. As for percentage data we had the percentage of people in different categories. An example of percentage data is the percentage of people in different age categories such as Age-0-9, Age-10-19 etc. Due to different nature of data, it required two different methods of fusion as shown in figure xx. Both the methods assume uniform distribution of data. For the value data first the spatial intersection was calculated between trip zones and mosaic/postal code zones. Secondly a portion of value based on area of intersection from the postal/mosaic was assigned to trip zone. For example if a mosaic area A has population 10 and if it lays 30% in trip zone X and 70% in trip zone Y then the population will be divided in such a way that X will have 3 people while Y will take the rest 7. As for percentage data, first all the areas which are intersecting a particular trip zones are added. If we have two areas of intersection a1 and a2, we get sum S = a1+a2. Secondly a weight is assigned to each of area with respect to its contribution to the sum. The weight w1 for area a1 will be calculated by dividing its area by sum: a1/S. In the third step a percentage value of each category in an area is multiplied with the weight calculated in previous step. In the final step all the values calculated for a particular category are summed to get the final value for the trip zone. How to match mosaic data into trip zones: Figure 2 Conceptual matching – contribution to each trip zone: value (left) and percentage (right)
  8. 8. Demand responsive Transit Service in the Stockholm Area 8 | P a g e 3.3. Database: 3.3.1 Set up database The database chosen for this project is PostgreSQL (8.4.1) in combination with its spatial extension PostGIS (1.5.3). 3.3.2 Creation & import of tables Almost all provided data was migrated into one big database to make inter relations easily possible. All the spatial data was additionally transformed to trip zone level beforehand, as explained above. Figure 3 Database and import of data From shape files All data, existing in ESRI shape format, was imported into postgresql using a plug-in for that purpose which is provided with PostGIS for the pgAdmin III tool. With this tool you create a new spatially extended table for saving the geometries of the shapes as well as all there related non-spatial attributes. For the geometry also the reference system needs to be specified to create corresponding constraints on the data set. From csv files To import the simulated travel demand, which was provided in matrix form, a small java tool was written. This tool can operate in two different modes. It can connect directly to the database and then reads all the information from the different file matrices populating the database with insert and update queries or it writes back the data in a new file that is in relational format and thus can easily be imported in the database using the copy command. The latter is to be preferred for large data sets due to the significant faster import time.
  9. 9. Demand responsive Transit Service in the Stockholm Area 9 | P a g e 3.4. Demand generation and distribution of DRT service Figure 4 Conceptual matching – contribution to each trip zone: percentage (left) and values (right) The figure above shows the conceptual tasks to fulfill to create a dynamic model of the demand expressed in terms of trips per day between the trip zones. The O/D matrices give an a-priori information about the travel behavior. That means we can get an idea from where to where people like to go and what time it takes. From the demographic data the potential customer can tried to be found and give some clue how many people would use the new service which can be interpreted as demand for the service. Combining those two sources in a meaningful way opens the possibility to then study the travel behavior of the potential customer. For this we chose the approach of the gravity model!
  10. 10. Demand responsive Transit Service in the Stockholm Area 10 | P a g e 3.4.1 Gravity model The general idea of a gravity model is that a trip from one zone i will be attracted to another zone j depending on the extent of activity in zone j related to a trip purpose and a friction factor between the zones. (Kutz, M, 2004) Tij = Trips between i and j Pi = Trips produced in zone i Aj = Trips attracted to zone j Fij = 1 / travel time A gravity model gives the opportunity to analyze potential flows by clustering analysis to find most interesting zones. Figure 5 Gravity model parameters i j ij ij j ij 1 PA F T A F n j  
  11. 11. Demand responsive Transit Service in the Stockholm Area 11 | P a g e Attraction The attraction is calculated as the sum of trips that are pointing to one zone, that is the total demand of every individual zone. Figure 6 Attraction Friction factor (Travel time) The friction between two zones is approximated with the inverse of the travel time. Longer time gives the higher friction. Find Trips generated by potential customers The main variables set identified as fundamental to point out a potential costumers group for DRT service are: - Individuals aged 21-39: they represent the working class, with high mobility needs. - High proportion of renters, non-family households and single-person households: this parameter is related with urban density, enlarging the numbers of people living in an area, and therefore the potential costumers. - Average vehicles per household. Researchers have shown the correlation between high density and limited numbers of car owners due to parking costs/congestion charges and availability of public services/shops. Dense cities, pedestrian and bicycle friendly are a key aspect for the success of sharing transport service (Andrew et al., 2006). - Level of education: people having high level of education shown a general inclination towards innovation, which characterize the DRT service.
  12. 12. Demand responsive Transit Service in the Stockholm Area 12 | P a g e - Level of income: DRT service is less expensive compared to taxi, but still more than public transport. People having medium-high level of income are likely to use more this service than others. Find the function: pattern by indicators (mosaic data attributes) Population Based Age distribution: 20-39 years Level of education: Bachelor or higher Number of cars/household: 0-1 Housing: Apartment block (indicator for high density districts) Income level: medium-high Geographic Based High density areas Land use mix/point of interest (bu ine cen e , comme ci l e , lei u e, chool, …) Although a potential customer can be found, five different potential customer profiles were created with varying age and income levels. This enables the choice of seeing how different profiles behave with different sets of parameters in a sensitivity analysis. Age Income 20 - 39 40 - 59 150 - 399 400+ 1 X X X X 2 X X 3 X X X 4 X X X 5 X X X Table 1 Potential customer profiles
  13. 13. Demand responsive Transit Service in the Stockholm Area 13 | P a g e AHP - weighting The attributes of our potential customer are correlated. To be able to determine an absolute number of people in one zone, without accounting for someone twice, AHP weighting was introduced. The pair wise comparison is set up with the criterion of correlation in an n*n matrix. Traditionally the scale for pair wise comparison is 1-9 implying an ordered scale but correlation takes values [1,-1] and we consider each attribute equal in value and it is only the weight that is our unknown parameter. By normalized columns averaging and normalize the rows of the matrix we can arrive at the priority matrix which gives the weight for each attribute. The sum of he weigh i lw y 1 nd he pe cen ge ep e en e ch i u e ’ relative value. (Karlsson J, Ryan K, 1997) It can be interpreted as that each attribute is one layer and each trip zone is one unit in the layer. The weights of the priority matrix are multiplied which respective attribute value and summed up to get number of absolute trips generated by potential customers per zone. Table 2 AHP example (priority matrix and resulting weights)
  14. 14. Demand responsive Transit Service in the Stockholm Area 14 | P a g e 3.5 Clustering Once the demand of the new service is distributed using the gravity model, the question rises how to interpret this data best and how to utilize this information to find the best areas for testing the service? This is where clustering is used. The problem we want to solve with our clustering is to find a small network of trip zones that have a strong possible usage of our service. The key features to be clustered are strong flow and a spatial component, that puts weight on the fact, that zones should neither be too close nor too far away from each other. Several methods for this purpose are available within literature for example the Ripleys K index or the K-Means clustering method. None of those was however directly available in the database. The first one was made available through an interface to use R a statistical programming tool within the database, but it turned out that this interface is far too slow to operate on our large data set. Due to this fact, we started developing our own clustering methods. 3.5.1 Clusteredness as a quality measure The two ideas we had for clustering are both based on the same idea of maximizing the flow within the cluster. That means the sum of all trips between the trip zones that are part of a cluster. From the figure below you can see an example of two clusters and how they would rank in relation to each other. Figure 7 Cluster ranking 3.5.2 Two own methods of clustering The two methods we developed are mainly differing in how to select those zones that are part of the cluster. The first proposed method starts from a zone and looks for the zone with the
  15. 15. Demand responsive Transit Service in the Stockholm Area 15 | P a g e highest flow to. This zone and the original zone are then considered as part of the cluster and the next zone is selected in a way that from both those zones the flow to the new zone is maximized again. This process continues until the final cluster size is reached. The second method is stronger based on heuristics and takes from the original zone the top destinations and calculates the clusteredness from this set of zones. The latter is computational more efficient but of course more of a guess than the first one. Comparison between both methods showed however that the resul don’ diffe oo much and thus the second one was chosen for the online application. Both methods can in theory be used in the application because the signature of their functions are identical in terms of input and output parameters. The figure below shows those parameters. Figure 8Clustering parameters and result The distance filter’ pu po e i o limi p i l ex ent of a cluster to reasonable extend and thus omit clusters scattered over the whole Stockholm area. The demand filter mainly omits negative demand created from a car density of more than one car per person in a zone. The cluster size finally also gives some limitations to the spatial extent.
  16. 16. Demand responsive Transit Service in the Stockholm Area 16 | P a g e 3.6. Visualization - Open Layers Apache Web server, PHP and open layers were used to create the web mapping application for visualization. Both spatial and non spatial data was fetched from PostgreSQL database in the form of xml. At the start of the application, upon client request all the basic data from trip zones is brought from the database by Apache Web Server and PHP. Afterwards the client uses open layers to renders the map. The spatial data in brought only once at the start. In the later stages of analysis only values of some attributes are dynamically changed to create different thematic maps. The visualization supports two main analyses. First one is concerned with viewing best ranked zones while the second one shows the cluster for a selected zone. The user is requested to give input for carrying out the analysis. The input parameter includes: Input variable Comments Demographic type Select one of the five types described earlier Top Best The number of best zones to display Distance Min, Max Distance filter to limit too close or too far zones Demand Min A threshold on minimum demand Cluster size The size for the cluster When a user provides these inputs, a sql based request is send through PHP to the database and the results are brought back to client in the form of xml. The client reads the xml and updates the values of attributes of trip-zone layer thus changing the thematic map to indicate the requested result. The visualized map also supported tools such as navigate, zoom, pan, and query. Figure 9 Interface in web application
  17. 17. Demand responsive Transit Service in the Stockholm Area 17 | P a g e Chapter 4 Results Since the main result of our project is not the analysis itself but a tool to perform it we show here in this section an example of one possible outcome of an analysis performed with our tool. Additionally also some of the preprocessed data that is major input to this analysis is presented. 4.1Analysis scenario As presented in the methodology section several different O/D matrices as an outcome of the gravity model were computed to represent the travel behavior of potential customers of the service. 5 different types are currently available. For the scenario type 4 was chosen because it has the strongest similarity to the type of customer found from the literature review. This is a young well educated person living in dense populated areas with middle to high income. To then perform the clustering analysis to find most suitable areas for the implementation of a pilot project several more parameters needs to be defined by the user. Those include the spatial extend and density of the cluster as well as the minimum demand for specific pairs of origin and destination. To make potential trips for the service not too short, a minimum average distance of 3 km was defined and to keep the extend still limit to a local scale of only a few neighborhoods, the maximum distance was limited to 8 km. To exclude all negative demand and very sparse used trips the demand minimum was set to 0.5 trips per day. The cluster was also set to consist of no more than 10 trip zones.
  18. 18. Demand responsive Transit Service in the Stockholm Area 18 | P a g e 4.2Scenario results Using this setup the following top-clustered areas were found: 1.) Sollentuna 2.) Hammarbyhöjden/Björkhagen 3.) Södertälje Figure Top 3 clusters (map from Eniro Dec 2010)
  19. 19. Demand responsive Transit Service in the Stockholm Area 19 | P a g e 4.2.1 Sollentuna result in detail The greater Sollentuna area had the highest demand with 235 trips/day. The cluster also includes interesting areas as Kista, Akalla and Husby and also Norrortsleden (road 265). To name a few features in the area of Kista hosts KTH and Stockholm university divisions. (kth.se, 2010) Perhaps foremost it is the home the Kista science city which is a world known ITC cluster. There is also a mall for shopping and restaurants. (kista.com, 2010). This makes it a center for many different groups of people as students, shoppers and businessmen apart from that the cluster also includes large residential areas. This variety of people would likely benefit a DRT service and covers a range of purposes. Kista area is connected by the close by passing of motorways E4 and E18, which is an advantage for the DRT service. On the downside, the southern zones of this cluster are already quite well connected by public transport as it lies along the blue subway line. Figure 10 Trip zone map with Sollentuna cluster highlighted (yellow)
  20. 20. Demand responsive Transit Service in the Stockholm Area 20 | P a g e Figure 11 Top five flows in Sollentuna cluster
  21. 21. Demand responsive Transit Service in the Stockholm Area 21 | P a g e 4.2.2 Hammarbyhöjden/Björkhagen result in detail Hammarbyhöjden/Björkhagen cluster is has the second highest demand of 228 trips/day. The origin zone and some of the other cluster zones Bagarmossen are along the green subway line. What is interesting with Hammarbyhöjden/Björkhagen cluster is that we can tell that some of the highest demand comes from Älta area and north of Älta (Lovisedal, Kolarängen) which are surrounded by a large nature area. These particular zones are connected by road 260 (north/south) and 229 (east/west passing Skärpnäck) but not by subway or commuter train which makes it slightly isolated. This is in our favor since the only public transport in these areas is bus. Figure 12 Trip zone map with Hammarbyhagen cluster highlighted (yellow)
  22. 22. Demand responsive Transit Service in the Stockholm Area 22 | P a g e Figure 13 Top five flows in Hammarbyhagen cluster
  23. 23. Demand responsive Transit Service in the Stockholm Area 23 | P a g e 4.2.3 Södertälje result in detail The origin zone of the third cluster with 213 trips/day is the area north of Södertälje. Most probably this is the demand of work related commuters of the western and northern outskirts of Södertälje. The cluster itself include zone with road E20 (East/West) and zones west of the central bridge. This indicates that there is demand both within Södertälje but also a possible false demand if E20 is included, people who are just passing Södertälje or start and will continue past Södertälje is included. Figure 14 Trip zone map with Södertälje cluster highlighted (yellow)
  24. 24. Demand responsive Transit Service in the Stockholm Area 24 | P a g e Figure 15 Top five flows in Södertälje cluster
  25. 25. Demand responsive Transit Service in the Stockholm Area 25 | P a g e 4.3General statistics of generated demand The two following tables show the absolute demand generated by our model as well as the percentage of population using our service according to it! A per trip zone aggregation can be found in the figure. Table 3 Absolut values for all types of generated demand Table 4 Relative values for all types of generated demand Figure 16 Aggregated in and outflow per trip zone using type 4 demand (other types can be found in appendix).
  26. 26. Demand responsive Transit Service in the Stockholm Area 26 | P a g e Chapter 5 Discussion 5.1 Resulting recommendation The scenario analysis suggested the Sollentuna solution to be the most suitable one for the implemen ion of pilo p ojec . hi i even hough i ’ w ju cen io ce inly one of the most interesting areas. And the parameters chosen for this scenario were not random but those that to our knowledge based from the literature review and discussions with Gyözö suits the wishes of the client best. One should still be aware of the fact that this is only one of many possible outcomes using our decision support tool. Another set of parameters might yield a completely different solution. At least regarding the Sollentuna area in contrast to for example Södertälje we found that it is strong in all demographic demand types which can be seen from the maps in the Appendix. 5.2 Methodology 5.2.1 Data Usage In the current model for the demand not all data available was used for different reasons, but if a suitable way to utilize this data as well can be found, then probably even better results can be achieved. Mosaic The Mosaic data contains the very interesting profiles of night and day population as percentages of the mosaic population groups. Those groups give very interesting information of what kind of people you can find in which areas. Due to the fact that this information is given in pe cen ge of num e we don’ know, n mely he c u l popul ion d y ime o at night time, it is impossible to translate this number into absolute demand as we did in the current model. We were thinking about using at least for night time population the given population of a postal code which we transformed on trip zone level as well as the percentages but we came to the conclusion that this can only be valid for mainly residential areas where it can be assumed that the biggest part of the population is staying at home at night time. Taxi The Taxi data is very valuable in the sense that this is real data not coming from a model and thus is not based on any assumptions. The problem with this data is, that it is not available for all zones it is thus spatially incomplete as well as that it only covers daytime and thus also is temporal incomplete. We tried however to get some estimates for taxi travel time for all zones by finding a relation between car travel time and taxi travel time and then use this relation to calculate the travel times for zones not covered in the taxi data set. It turned out however, that the correlation between those to variables was very low (around 0.2) so that it is impossible to calculate statistical reliable estimates from that information.
  27. 27. Demand responsive Transit Service in the Stockholm Area 27 | P a g e Public transport We have the locations of a great number of bus stops as well as for the subway and trains and this information shows clearly that they are well distributed over the whole city. Areas which are already well connected through public transport are probably more unlikely to use the new DRT service and some analysis maybe based on the distance to public transport stops should be included to give this credit and make the results thus more reliable. 5.2.2 Clustering The clustering method currently used is heavily based on heuristics and therefore c n’ e treated as a exact measure. There are many other clustering methods available and it might be worthwhile to put effort into evaluating those further. We see clustering as one of the most important methods to find well connected zones and thus a perfect tool to select the pilot area. Maybe it is even worth to rewrite some of the existing algorithms in a database function to increase the computational efficiency. As stated before the approach to utilize external programs through interfaces turned out to be far too slow to be considered useful. 5.2.3 Travelling is no purpose The analysis now is based on the travel pattern provided from the O/D matrices. It is however not known to us under which exact assumptions those are created and thus not possible to derive more distinct purposes of travelling from those patterns. The knowledge about purpose connected to the information of which kind of people are living in specific zones can give very deep insight about the travel behavior. A possible solution might be to use the point of interest data or even an extended version of it since the current version only includes data about night time activities. Using this data for the attraction in a gravity model which distinguishes between the different population groups can give a very distinct purpose based travel pattern. 5.2.4 Price politics A very important component for the acceptance of this service is also the financial component for the customer. It should take into account the prices for public transport and other similar services in the study area. The price can of cause be higher than of public transport given credit to the higher convenience factor. 5.2.5 Time In the current model the peak hour time is used as the friction factor between zones in the gravity model. A more detailed analysis taking into account the two peaks during a day and the off peak hours can give a more realistic model about when people are where. However, fo he ove ll dyn mic of he zone i doe n’ pl y n impo n ole nd does not change the outcome of our current analysis.
  28. 28. Demand responsive Transit Service in the Stockholm Area 28 | P a g e Chapter 6 Conclusion We can finally conclude that we succeeded in creating a web application for finding suitable areas for a pilot project of a DRT service in the Stockholm area! The current version gives the customer the possibility to run the sensitivity analysis on 5 different data sets. Based on the chosen clustering parameters he can find both a top ranking of best zones as well as some more detailed information about the clusters those top zones belong to. Of cause there is still a lot of room for improvement both in the interaction with data over the interface as well as with the underlying data itself. One nice future feature would be to generate the demand on the fly based on a selection of demographic parameters as well as some other influencing factors like the price and accessibility of public transport etc. For both the latter factors realistic models needs first to be created. Another point is the clustering which is now a very fast but not very reliable clustering. The potential of other methods should be investigated, both in terms of the clustering result itself as well as the performance when applied to the complete data set.
  29. 29. Demand responsive Transit Service in the Stockholm Area 29 | P a g e References Andrew J., Douma F., Developing a Model for Car Sharing Potential in Twin Cities Neighborhoods, Transportation Research Board 85th Annual Meeting, 2006 (www.trb.org); available at www.mdt.mt.gov/research/docs/trb_cd/Files/06-2449.pdf. Celsor C., Millard-Ball A., Where Does Car-Sharing Work? Using Gis To Assess Market Potential, Annual Meeting of the Transportation Research Board, 2007 EU Project Penelope (Promoting ENergy Efficiency to Local Organisations through dissemination Partnerships in Europe), Demand Responsive Transit service (DRTs): PersonalBus - Tuscany - Florence – Italy, Report, 3 September 2002 Horn M.E.T., Multi-modal and demand-responsive passenger transport system: a modeling framework with embedded control system, Transport Research Part A, 36, 2002, Pages 167- 188 Hua T.-L., Sheu J.-B., A fuzzy-based customer classification method for demand-responsive logistical distribution operations, Fuzzy Sets and Systems, Volume 139, Issue 2, 16 October 2003, Pages 431-450 Karlsson J, Ryan K, A Cost-Value approach for prioritizing requirements, IEEE Software, 1997, pp 67-74 Kista Science City, http://www.kista.com, 2010-12-20 KTH, http://www.kth.se/ict/campusitu/campus-kista-1.7534, 2010-12-20 Kutz, M, Handbook of transportation Engineering, New York: MacGraw-Hill, 2004 Mageean J., Nelson J. D., The evaluation of demand responsive transport services in Europe, Journal of Transport Geography 11, 2003, Pages 255–270 Palmer K., Dessouky M., Abdelmaguid T., Impacts of management practices and advanced technologies on demand responsive transit systems, Transportation Research Part A, 38, 2004, Pages 495–509 PostGIS, http://www.postgis.org , 2010-12-19 PostgreSQL, http://www.postgresql.org , 2010-12-19 Quadrifoglio L., Xiugang L., A methodology to derive the critical demand density for designing and operating feeder transit services, Transportation Research Part B, 43, 2009, Pages 922–935 Quadrifoglio L., Maged M. Dessouky, Fernando Ordonez, Transportation Research Part A, 42, 2008, Pages 718–737
  30. 30. Demand responsive Transit Service in the Stockholm Area 30 | P a g e Stockholms län. http://www.ne.se/kort/stockholms-län, Nationalencyklopedin, 2010-12-17. Storstockholms Lokaltrafik, www.sl.se, 2010-12-17. A simulation study of demand responsive transit system design. Yim Y.B., Khattak Asad J., Personalized Demand Responsive Transit Systems, California Path Program Institute Of Transportation Studies University Of California, Berkeley, October 2000
  31. 31. Demand responsive Transit Service in the Stockholm Area 31 | P a g e APPENDIX:
  32. 32. Demand responsive Transit Service in the Stockholm Area 32 | P a g e