SMT201 - Geographic Information Systems for Urban Planning (G1) Project Report Specially Prepared for: Professor Kam Tin Seong Prepared by: Group 4 Kang Hui Yun [S9642400J] Lee Jung Jae [G1508022T] Wang Youjin [G1107574L] Date of Submission: 24 th November 2019 Academic Year 2018/19 – Term 1 1 Table of Contents Abstract ................................ ................................ ................................ ................................ ........... 2 1. Introduction ................................ ................................ ................................ ............................... 2 2. Data Collection and P rep rocessing ................................ ................................ .......................... 2 3. Methodology ................................ ................................ ................................ .............................. 3 4. Change Detection ................................ ................................ ................................ ...................... 3 5. Master P lan Land Changes ................................ ................................ ................................ ...... 3 6. Population Changes and Forecast ................................ ................................ ........................... 5 7. Analysis and Recommendations ................................ ................................ .............................. 6 7.1 Data Preparation ................................ ................................ ................................ .................... 7 7.2 Suitability Analysis for Schools ................................ ................................ ............................ 8 7.3 Suitability Analysis for Eldercare Centres ................................ ................................ ............ 9 7.4 Suitability Analysis for Shopping Mall ................................ ................................ ............... 11 8. Limitations ................................ ................................ ................................ ............................... 15 8.1 Scope of Project ................................ ................................ ................................ .................. 15 8.2 Data Availability ................................ ................................ ................................ ................. 15 8.3 Time Constraint ................................ ................................ ................................ ................... 15 References ................................ ................................ ................................ ................................ ..... 16 2 Abstract — The motivation of this project is to harness the growing power and efficacy of Geographic Information Systems (GIS) tools to derive useful insights in urban planning. Specifically, GIS allows us to gain a higher understanding of our study area, and subseq uently take it a step further to ensure the safety and overall welfare of the residents in Yishun. By making use of various data extracted from government sources such as SingStat and Data.gov, we aim to provide the uncovered insights and multiple analyses of the Yishun planning area in terms of the following: 1) Changes in the landscapes of Yishun and their purposes, 2) Population demographics and percentage changes, and lastly, 3) Analysis and recommendations in the interest of future planning across all age groups identified in Yishun. 1. INTRODUCTION FIGURE 1. Yishun planning area in Singapore Situated at the north of Singapore, Yishun was previously known as Nee Soon — the Mandarin romanisation of 林 义顺 (Lim Nee Soon). He was a successful industri alist, being a rubber and pineapple magnate in the region 1 . Overtime, several roads and ultimately the planning area we know today are named after him. With a total of 9 different subzones existing within Yishun, the land area estimates around 21.24 km². I t ranks 8th, 7th, and 20th in terms of size, population and density respectively, out of the 55 planning areas in Singapore. There is generally an increasing trend for population in Yishun but is especially high for senior citizens aged 65 and above. With population trends and changes among different age groups considered, it is essential to use Geographic Information Systems tools to highlight and recommend for future development plans of Yishun area. FIGURE 1. Yishun planning area (with subzones) extrac ted using selection by expression 2. DATA COLLECTION AND PREPROCESSING With the growing availability of data, we have gathered our preliminary and secondary data from multiple sources, ranging from governmental sites to web articles and Wikipedia for usage in both QGIS and also to use the non - spatial information to better our initial understanding of Yishun. We have acquired the base layers (i.e. shapefiles of Yishun’s Planning Area/Subzone boundary, Master Plan 2008 & 2014 Land Use, and so forth) from Data.gov 2 , which provides mainly spatial data that can be imported and visualised in many GIS software. For aspatial (non - spatial) data such as CSV (comma - separated values) files that serves to enhance the quality of the former, we have obtained them from SingStat 3 . Most of the time, these CSV files are manipulated both internally (in QGIS) and externally to be joined with the relevant layers for further analyses. 3 In our acknowledgement, we have also used data available in the course, which are obtained fr om Land Transport DataMall 4 , OpenStreetMap 5 and BBBike@Singapore 6 3. METHODOLOGY To achieve the final product in QGIS, various tools and features are used and leveraged on. In fact, many of the layers went through data manipulation and several iterations of spatial preprocessing before the desired output is produced for human analysis. As the number of tools and functions used are almost boundless, we will name a few to showcase the ability of QGIS: For vector layers: 1. Clip 2. Buffer 3. Difference 4. Intersection 5. Multipart to Singleparts 6. Geocoding For raster layers: 1. Rasterize 2. Raster Calculator 3. Proximity (Raster Distance) 4. Clip Raster with Polygon 5. Iso - Area as Contours (from Layer) Others: 1. Virtual layers 2. Structured Query Language (SQL) 3. Joins (with other l ayers) 4. CHANGE DETECTION One of our first objectives in the Master Plan study is to identify the changes over the years that has took place in Yishun. The change detection will cover the fundamental differential observation of the land (including land use), as well as other aspects such as population and demographics. We will dive into the individual analysis in each section below. It is also important to note that the various analyses provided in the subsequent sections pertains to the changes between 2008 and 2014, unless otherwise stated. 5. MASTER PLAN LAND CHANGES The Master Plan proposed by the Singapore government is paramount in guiding the country’s development of land and property with a long - term outlook. Needless to say, it is one of the key fac tors in deciding where the country stands in the future, for it holds a major stake in the welfare of our residents through the many ways that it can affect the different aspects of life. For example, proper land management can change how people live — wheth er they stay in a HDB flat or condominium, if their children can enrol in a public school, and so on. Undoubtedly, it will also affect Singapore’s tourism industry and naturally the economy as a whole. FIGURE 2. Depiction of boundary changes in Yishun between 2008 and 2014 Figure 2 illustrates the change in Yishun’s boundary between Master Plan 2008 and 2014. We observe a reduction of land from the north - east direction where majority of the area diminishes from the east side. Subzones affected a re namely Northland, Yishun East and Lower Seletar. Another crucial aspect of land changes lies in the allocation of land use. As mentioned before, and with the introduction of Master Plan, Singapore aims to construct itself as a sustainable city - state by balancing economic, social and environmental factors 7 . This is made possible with long - term monitoring of the country’s growth which allows the government to carry out careful assignment of land use, making sure its limited land is optimised 4 for the right purposes. FIGURE 3. Land use of Yishun in Master Plan 2008 As it is rather difficult to tell the differences purely based on a side - by - side map comparison, this paper will highlight the major changes of land use between the two years in textual and tab ular forms. To at least give an overview of Yishun’s land use, Figure 3 shows the landscape of the planning area in the 2008 Master Plan with various purposes attributed to identified plots of land. Altogether, there are a total of 21 categories for land u se. One of the basic yet powerful functions of QGIS is the availability of data manipulation through the popular Structured Query Language (SQL) used in databases today. In order to derive accurate figures of land use, we have made use of such SQL in the c reation of a virtual layer: SELECT "LU_DESC", COUNT(*), SUM(SHAPE_Area) FROM LandUse_08_Yishun GROUP BY "LU_DESC" The block of SQL above is used to aggregate the land use information of Yishun in Master Plan 2008. “LU_DESC” specifies the category of land u se — e.g. Business 1, while COUNT(*) tells us the number of occurrences and SUM(SHAPE_Area) adds up the area under each category, which is enabled by the GROUP BY statement. Upon doing the same for Master Plan 2014, we exported the data into an Excel spreadsheet for further analysis using its native features such as filtering, sorting and generation of charts. Land Use Change in Area (m 2 ) Reserve Site 477,850 Park 150,698 Road 81,174 TABLE 1. Top 3 categories with highest increase in size Land Use Change in Area (m 2 ) Sports & Recreation - 1,087,953 Waterbody - 287,940 Residential - 276,628 TABLE 2. Top 3 categories with highest decrease in size Table 1 and 2 highlight the top 3 categories of land use that saw the most change in area in terms of increment and decrement respectively. There are also certain interesting observation from the change in land use. Intuitively, one would foresee an expansion in land area whenever the number of occurrences (i.e. count) for the land use increases a nd vice versa. Land Use Change in Change in 5 Number Area (m 2 ) Residential 81 - 276,628 Waterbody 5 - 287,940 Business 1 2 - 66,123 Transport Facilities 0 - 14,287 Special Use 0 - 4,129 Place of Worship 0 - 2,628 Reserve Site - 2 477,850 TABLE 3. Land use with contrasting figures However, from Table 3 we can deduce otherwise. Although the number of residential lands increased by a whopping count of 81, its total land area decreased by 276,628m 2 . On the other hand, the number of reserve sites had decreas ed by 2 while its area increased relatively drastically, measuring at 477,850m 2 . On a side note (not included in the table), Residential with Commercial at 1st Storey saw an increase of 5m 2 albeit there was no change in its number of land. Nonetheless, the overall pattern remains intuitive — an increase land area follows the increase in number of land use. 13 out of the 21 categories account for such cases. 6. POPULATION CHANGES AND FORECAST After change detection of land use between 2008 and 2014, the next step was to import Yishun population CSV files (both 2008 & 2014) into QGIS and join them to Yishun shapefile maps. For our analysis, we have classified the population data into three different groups: Young (0 - 24), Economic Active (25 - 64), and Aged (6 5 and above). To calculate the population percentage change between the two years, the following formula was used: (population 2014 - population 2008) / population 2008 FIGURE 4. Percentage change of the Young (0 - 24) population between 2008 and 2014 As shown from the figure above, there is a decrease in the young population in 5 out of 9 subzones of Yishun which are adjacent to each other. The biggest decrease in terms of numbers took place in Yishun West with a drop of approximately 3,010 residents. All in all, the young group has seen a decrease by approximately 5,000 residents in the period of 2008 to 2014. Figure 5. Percentage change of the Economic Active (25 - 64) population between 2008 and 2014 Despite an overall population increase throughou t the subzones for the Economic Active group, it is relatively minimal except for Yishun East. There was an approximately 46% (~8,550 residents) increase for the subzone. Although there does not exist a clear - cut justification for such phenomenon, one impo rtant factor that such happening can be attributed to is that almost all business areas are heavily concentrated in Northland, making Yishun 6 West a fairly compatible place for the Economic Active group population to reside. Figure 6. Employment in Yishun by industry (2010) Figure 7. Employment in Yishun by industry (2015) To gain further insights on the Economic Active group of Yishun, we have extracted the employment census data for 2010 and 2015 from SingStat and created two pie charts for each year. From both years, we can see that the top 4 industries are: Wholesale & Retail Trade, Manufacturing, Public Administration & Education, and Transportation & Storage. Figure 8. Percentage change of the Aged (65 & above) population between 2008 and 2014 The Aged group showed the largest percentage change among the three population groups in Yishun. This means that the darker marked subzones saw a sudden jump of headcounts, of up to about 100% (which means double the original count) compared to the other two groups, with a maximum percentage change capped at 50% (increase of half the population). Figure 9. Yishun’s population trend and forecast (2001 - 2024) To summarise the population trend and to predict the future growth for the three age groups of Yishun, we have extracted the population census data of Singapore from SingStat and used the appropriate trendlines in Excel to derive our forecasts. From the il lustration (Figure 9), it can be deduced that the population growth for the Economic Active and Aged groups will continue to increase, whereas the Young group will gradually decline over time. Overall, the total population of Yishun shall increase steadily , hitting a foreseeable figure of 226,009 residents by 2024. 7. ANALYSIS AND RECOMMENDATIONS Based on the data collected and the forecasted demand of the future population, we built a descriptive analysis model to better understand the accessibility of the th ree age groups to different facilities — mainly to provide the key social needs, namely, schools for the Young, shopping malls for the Economic Active and eldercare centres for the Aged. The output of the model is used as referential 7 layer, and is further co nsidered with other factors to derive the final suitability land lot map for each case. Regardless of age groups, accessibility is an important factor and is a top priority in many cases, especially for a fast - paced country like Singapore. For instance, el dercare centres and schools should be easily accessible through the use of public transportation, which are the various bus stops located in the planning area. We also take into consideration the other important social factors in building the new public fa cilities. However, the importance of each of them is subjective hence, differs from person to person. Lastly, the creation of the model is based on the understanding and knowledge we obtained from various public datasets, hence it can be oversimplified and may contain the team’s assumptions whenever there is a lack of information. Nevertheless, these models should serve to provide a good insight of the happenings in the Yishun planning area. 7.1 DATA PREPARATION Some SHP and KML layers from Data.gov are not up to date, including information on bus stops, food courts and markets. Hence, in the data preparation stage, we cross - reference the data obtained from Data.gov with Google Map 8 and OpenStreetMap to achieve the highest possible accuracy in our analyses. In addition, manual geocoding is involved in the preparation. We also tidy up the raw datasets in the way that suit the analysis we carry out in each given scenario: 1) Buildings: in the land suitability analysis, we are only concerned about the residential/business properties whereas buildings used for other purposes are not taken into consideration. This is because we aim to conduct proximity analysis to ensure high acc essibility to the different age groups. In other words, we want schools and eldercare centres to be built near the vicinity of the homes, and the mall to be close to the workplaces. However, the challenge being many building data points do not state their types, we have to resort to a close alternative — clip the building layer to the residential area indicated in the land use dataset, which we treat as the actual places of residence. Figure 10. Buildings (filtered vs. unfiltered) 2) Roads: the road data is us ed for the purpose of avoiding heavy traffic. This is particularly true in the case of the site selections of eldercare centres, as we think that safety is one of the priorities of the Aged. Hence, we extracted the road types: primary, primary link, second ary, secondary link, motorway and motorway link. These are the roads we deem to be dangerous with high risk of accidents due to the nature of the roads. Figure 11. Roads identified with heavy traffic 3) Residential area: As mentioned before, we wish for better accessibility for the Young and Aged group so that they could reach the target locations safely and in a short amount of time. The selected areas are that of residential, commercial & residential and residential with commercial at 1 st storey (from l and use data). 8 Figure 12. Land areas used for residential purposes 4) Point - of - interest (POI): A layer we call social POIs is used in the suitability analysis for shopping mall. Data is obtained from OpenStreetMap. However, in order to get the accurate POIs in the context of Economic Active group, we manually filtered out the POIs that have stronger relevance to the population’s lifestyle including food centres, cinemas and hawker centres. Figure 13. Social POIs (filtered vs. unfiltered) 7.2 SUITABILITY AN ALYSIS FOR SCHOOLS From our population prediction of Yishun, the Young group is forecasted to have a declining trend, overall. However, there are 2 subzones that defy this general trend based on the observation of our population analysis. After analysing t he locations and number of educational institutions in Yishun, we proposed to have 2 more schools built in such subzones. We also recommend building the schools within an accessible range to the public library. Distance to bus stops and residential buildin gs are used as the base of the binary model. a. It should be near to the residential areas b. It should be close to the bus stops 7.2.1 BUS STOPS We first rasterized the bus stop vector layer to serve as an input for the next step. The proximity map is then created based on these raster locations of bus stops, taking them as nodes. Students mainly travel by bus as a means of daily commute. Hence, a walking - friendly distance from school to the bus stops is strongly desired. Category Proximity Range (m) Accessible Less than or equal to 250 Poor Accessibility Greater than 250 TABLE 4. Accessibility table of schools and bus stops Figure 14. Bus stops (reclassified) 7.2.2 RESIDENTIAL AREA As parents mostly prefer their children to enrol in neighbourhood schools and that Singapore is a small country, students tend to return to their abode at the end of the day. This is unlike situations where bigger countries have poorer and/or complicated a ccessibility. In this context, it is not uncommon for students in such countries to move out of their house to stay in a nearby dormitory during the period of schooling. For this, we propose to have schools built near residential areas which also 9 makes it easier for parents to walk their young children to school before heading to work. Category Proximity Range (m) Accessible Less or equal to 250 Poor Accessibility Greater than 250 TABLE 5. Accessibility table of schools and residential areas Figure 15. Residential buildings (reclassified) Besides the factors considered in the binary map, we also refer to other significant benchmarks that make up an ideal school location, such as the availability and proximity to a public library. We carried ou t the service area analysis using Iso - Area Map from QNEAT3, an external plugin is used for advanced network analysis. Figure 16. Network analysis of public library Finally, the location for the new schools were selected in the subzone that is forecasted to have a significant increase in the young population with years to come. Figure 17. Suitability map showing 2 ideal locations for building schools 7.3 SUITABILIT Y ANALYSIS FOR ELDERCARE CENTRES Previously, we have identified an overall increase in the Aged population. In addition, there is also a lack of eldercare centres in the west and south region of Yishun. We believe that the ageing population will be an ongo ing event based on history and the current situation in Singapore. Therefore, it is safe to say that the demand for such a facility will soar. Given this, we have proposed to build another 3 eldercare centres in the planning area. However, due to the lack of information, we have made an assumption that the number of eldercare centres suffice for the Aged population in the latest year, and do our extrapolation forecasting to derive the final number of 3. 7.3.1 BUS STOPS Based on statistics, a majority of the elderly travel around short distance trips by bus 9 . Hence, the proximity to bus stops in the planning area is key to determine the most suitable location for new eldercare centres. Category Proximity Range (m) 10 Accessible Less than or equal to 250 Poor Accessibility Greater than 250 TABLE 6. Accessibility table of eldercare centres and bus stops Figure 18. Bus stop (reclassified) 7.3.2 NATURE The current 3 eldercare centres in Yishun are densely located in the east region and are far away from the rich natural locations of Yishun. We believe it is crucial for the eldercare centres to be close to nature as various studies demonstrated the positive impact that nature can bring to human being’s happiness index. Hence, as an improvement to the curre nt eldercare infrastructure in Yishun, we proposed to have the new facilities built within a good distance to the natural features. We also believe that the previous generation favours such places as it suits their daily interests and lifestyle. Category Proximity Range (m) Accessible Less than or equal to 250 Poor Accessibility Greater than 250 TABLE 7. Accessibility table of eldercare centres and natural features Figure 19. Natural features (reclassified) 7.3.3 SLOPE From the initial slope analysis of the whole planning area, we found out many roads within residential area in Yishun have steep slopes, which is not desired as the elderly should avoid being put in danger. With that said, the new eldercare centres proposed should be furth er away from the steep areas/roads. Category Slope Steepness (degree) Elderly - friendly Less than or equal to 15 Not friendly Greater than 15 TABLE 8. Classification of slope Figure 20. Slope analysis (reclassified) 11 Putting the reclassified output of all three decision factors together, we derived the below binary suitability map for eldercare centres. Figure 21. Binary suitability map for eldercare centres As illustrated above, the white areas represent the suitable land lots. On top of that, we also want to make sure that the new centre is within a ‘safe’ area coverage to the public hospital in the event of an emergency. Hence, we did a network analysis on the public hospital in Yishun before recommending the final locations of the eldercare cent res. Each circle represents 500 meters travel via the roads indicated based on road section line. Figure 22. Service area analysis for hospital In addition, we applied population study by subzone to find out the area that is most suitable for the new cons truction project. As shown below, there exists a subzone on the upper west area that has a high count of elderly members but have not had any eldercare facilities present. Figure 23. Population count of the Aged group Combining the binary suitability anal ysis output map together with other important factors, we derived the final map. Our proposal to build 3 eldercare centres is shown in the figure below. Figure 24. Suitability map showing 3 ideal locations for building eldercare centres 7.4 SUITABILITY AN ALYSIS FOR SHOPPING MALL Given the future development for Yishun as stated in the LTA development paper for Yishun in 2018, more business opportunities will be created in the upcoming years. On top of this, we also identified that there is a significant in crease of the Economic 12 Active age group in the north and east region of the Yishun. Hence, we proposed to build a new shopping mall since there is currently only one prominent shopping mall serving the entire local residence. Three decision factors were us ed in the derivation of the suitability map. Naturally, these factors have a differing importance when considering where to build the mall. Hence, unlike the previous analyses performed for schools and eldercare centres, we used a multi - factor analysis met hod with the help of Analytic Hierarchy Process (AHP). The common practice or rather methodology we applied in our suitability analysis is the rasterization of the initial vector layer, after which we performed proximity calculation to show the closeness of all features in Yishun to the identified features of interests. As shown in the map, the darker parts depict higher proximity to the features of interest (social POIs, public transport, and business areas). Figure 25. Proximity to MRT Figure 26. Pro ximity to business area (workplaces) Figure 27. Proximity to social POIs Upon completion of the proximity analyses done on the 3 selected features of interest, we next quantified the suitability by giving them a score that we can comprehend. Hence, applying the standardisation technique frequently used in statistics, we have employed the Max - Min method to standardise the above analysis results: Standardised Results = [Proximity to Features - Min(Proximity to Features)] / [Max(Proximity to Features) - Min(Proximity to Features)] The calculation is done using Raster Calculator in the processing tool. The x value will be the attribute available in the selection. We then manually extracted the max/min obtained from the proximity step. The final standardised results ranged from 0 - 1 and 13 we categorized them into 10 classes to give a clearer understanding of the score distribution performance. Figure 28. Proximity to social POIs (standardised) Figure 29. Proximity to MRTs (standardised) Figure 30 Proximity to business area/buildings (standardised) 7.4.1 SOCIAL POIs One of the key factors we need to consider in the analysis was the existing social point of interests for this age group (restaurants, food courts, cinema, etc). The shopping mall shou ld be built near these places to shorten the movement distance required for this age group. Figure 31. Social POIs map (standardised) 7.4.2 PUBLIC TRANSPORT Given the money and time that are needed to put in the shopping mall construction, it’s crucial to have the new shopping mall built near to the MRT station, ensuring easy access for public - especially to people living outside of Yishun. Given the current MRT stations in Yishun, we carry out the proximity analysis and standardise the value us ing raster calculator. Figure 32. MRT accessibility map (standardised) 7.4.3 BUSINESS AREA As the population in discussion is the Economic Active group, our focus will be on the business area of Yishun where the offices are designated. We 14 propose to make it accessible for people working in the area to travel to the new shopping mall. Figure 33 Business area accessibility map (standardised) Putting three decision factors together, we concluded that social factor is of utmost importance to the new shopping mall site selection, followed by accessibility factor 1 which is the distance to public tr ansport, namely the MRT station, and then the distance to business area(their workplace). Hence, in the AHP table, we assign the below scores to each decision factor to derive their weightage. Figure 34. AHP (relative importance of the factors) Figure 35. AHP weightage and consistency check Figure 36. Composite suitability analysis using raster calculator On top of the three main decision factors, similar to the suitability analysis for schools, we also applied the population distribution by subzone a nd the changes. The yellow dotted subzone as shown below is identified as the area as the most appropriate to host the new construction projection given the growth of the economic active population being 42%. 15 Figure 37. Population count of the Economic A ctive group Putting all the factors together, we then derived the final suitability analysis map as below. We decided to build one shopping mall due to the complexity and cost involved in building a facility like that of a shopping mall. Figure 38. Suitability map showing the ideal location for building a shopping mall 8. LIMITATIONS 8.1 SCOPE OF PROJECT In the suitability analysis, we only focused on the Yishun planning area alone without taking its surrounding neighbours into consideration. This is due to the scope of our study which is narrowed down to only one planning area — Yishun. However, in order fo r the new construction project to be successful and beneficial to the people living in and out of Yishun, we need to look at adjacent planning areas too to balance out the facility distribution. For example, there are very few eldercare centres located in Sembawang despite its large ageing population. Hence, we might be able to build one in Yishun that can also serve the population living in Sembawang as well. 8.2 DATA AVAILABILITY From the census data, we have identified the general trend for each age gro up. However, due to the lack of up - to - date and certain demographic data specific to different categories, we were not able to achieve an even better analysis and projection. If a more specific and updated dataset can be provided, we can potentially factor them into our analysis and provide better suggestions and recommendations that will best represent the status quo of the future. 8.3 TIME CONSTRAINT There are more insightful analys e s that can be done to the land suitability for different age groups. Due t o time constraints however, we only focus on a limited number of key factors. 16 REFERENCES 1. Wikipedia, “Yishun”, Accessed November 24, 2019. https://en.wikipedia.org/wiki/Yishun 2. Data.gov.sg, (Datasets), Accessed November 2019. https://data.gov.sg/ 3. Singapore Department of Statistics, (Datasets), Accessed November 2019. https://www.singstat.gov.sg/ 4. Land Transport DataMall, (Datasets), Accessed November 2019. https://www.mytransport.sg/content/mytranspo rt/home/dataMall.html 5. OpenStr eetMap, (Datasets), Accessed November 2019. https://www.openstreetmap.org/#map=12/1.36 17/103.8558&layers=T 6. BBBike, (Datasets), Accessed November 2019. https://www.bbbike.org/Singapore/ 7. Urban Redevelopment Authority, “Planning”, Accessed November 24, 2019. https://www.ura.gov.sg/Corporate/Planning 8. Google Maps. Accessed November 2019. https://www.google.com.sg/maps/ 9. Justin Ong, “NUS public transport study: Working adults want reliable travel times; seniors, students focus on cost”Accessed November 24 20 19. https://www.todayonline.com/working - adults - want - reliable - travel - times - public - transport - while - seniors - students - focus - fare - cost