Bus Karo: Developing Supporting Ecosystems
•Als PPTX, PDF herunterladen•
0 gefällt mir•588 views
This document discusses developing supporting ecosystems to improve city bus services. It summarizes a presentation given by Archana Ramakrishnan of Xerox Innovation Group at a workshop on urban mobility. The presentation discusses trends in urban mobility, including rising populations in cities and a shift away from private car ownership among younger generations. It outlines opportunities for public-private partnerships and mobility-as-a-service models to integrate various transportation options. The presentation also provides examples of how mobility data and analytics can help optimize bus routes and schedules to improve ridership, efficiency and customer experience.
Empfohlen
Empfohlen
Weitere ähnliche Inhalte
Was ist angesagt?
Was ist angesagt? (20)
Andere mochten auch
Andere mochten auch (19)
Ähnlich wie Bus Karo: Developing Supporting Ecosystems
Ähnlich wie Bus Karo: Developing Supporting Ecosystems (20)
Mehr von WRI India
Mehr von WRI India (20)
Kürzlich hochgeladen
Kürzlich hochgeladen (20)
Bus Karo: Developing Supporting Ecosystems
- 1. ©2013 Xerox Corporation. All rights reserved. Xerox® and Xerox Design® are trademarks of Xerox Corporation in the United States and/or other countries. Developing Supporting Ecosystems to Improve City Bus Services Archana Ramakrishnan, Xerox Innovation Group WRI India - Bus Karo Workshop 18th Nov, 2016
- 2. 2 November 21, 2016 Xerox Internal Use Only Total number of vehicles in Bengaluru breaching the 60-lakh mark this year
- 3. Xerox Confidential What is driving growth of Urban Mobility? 18-24 year olds will represent 50% of urban workforce by 2025 how to serve them? Less reliant on cars; mobility decisions on-the- fly Drivers of the Sharing Economy 3 November 21, 2016 Xerox Internal Use Only WalkingBike Sharing Mass Transit Car Sharing Ride Sharing Parking Motor Scooters Taxis
- 4. By 2025 4 November 21, 2016 Xerox Internal Use Only 51% Will decide where to live and work based on transport 50% One app for all transport needs 37% Will use an electric car 32% Will use a self driving car 41% Will not use cash to pay for transport
- 5. Private sector driving growth in mobility 5 November 21, 2016 Xerox Internal Use Only
- 6. Mobility As-A-Service: Making public transport a preferred choice 6 November 21, 2016 Xerox Internal Use Only
- 7. Benefits Enable better last mile connectivity. Seamless customer experience & Improved ridership New business models Improve network efficiency Optimize operations and lower cost Become more demand responsive. 7 November 21, 2016 Xerox Internal Use Only
- 8. • Spatial & Temporal Profiling • Occupancy Analysis • Sales & loyalty Analysis • Price Simulation • Revenue Optimization • Traveller behaviours • Vehicle Load • OriginDestination • On Time performance • Network connectivity • Simulation • Optimization A single platform to process, analyse, visualize and optimize all mobility needs Xerox Mobility Analytics Platform Mobility As- A-Service TOLLING / Road Traffic Parking Public Transit Enabled by a underlying analytics platform
- 9. Origin Destination analysis Feature : • Build origin and destinations matrices from the fare collection data Benefits : • Understand the demand in details at any time of day and at any date Advantages : • No need for OD surveys • Global coverage of the population • Continuously up to date Page 9
- 10. Vehicle load estimation Feature : • Estimate each vehicle load from the fare collection data Benefits : • Identify underused and overloaded services at any time of day and on any line segment Advantages : • No need for APC systems or manual counting campaign • Global coverage of the fleet • Continuously up to date Page 10 Focus
- 11. Travel times analysis Feature : • Compute any point access times at any moment from the therotical schedules or from actual trips data Benefits : • Identify precisely areas and periods with limited accessibility from public transit Advantages : • Instant computation allows very dynamic analysis • Can work with actual service trips timings to assess real access times Page 11
- 12. Towards a Demand Responsive Service 12 November 21, 2016 Xerox Internal Use Only IPK Vehicle Utilization Ridership Revenue Increases Passenger Wait Time Cost of operation Congestion & Pollution Bus schedules drafted in sync with the demand
- 13. Case study: Latin American City • BRTS system • 35 bus stops • 27 km route distance • ~ 0.3 million passengers per day • Multiple services on the same line
- 14. Case study: Ticketing system • Card based swipe-in • Swipe-in enabled gates at the bus stop entrance • Time and card id recorded during swipe-in • Flat fare deducted with no swipe-out • Time of swipe-in allows to estimate the waiting time Objective: Minimize percentage of commuters experiencing a wait time of > 5mins.
- 15. Results Scenarios % > 5mins # of trips # of buses Avg. Waiting time( in mins) Current 36% 356 81 10 Schedule A 30% 356 66 9 Schedule B 16% 568 81 5 Schedule C 15% 579 85 5 Schedule D 35% 300 61 10 Schedule built on first half of Jan 2015 and tested on second half Note: Here 1% of difference corresponds to around 3400 people per day
- 16. Commuter Feedback Mechanisms : An enabler for improving service qualities 16 November 21, 2016 Xerox Internal Use Only
- 17. Enable urban informatics from crowdsourced resident feedback by leveraging eco-system of platforms Enable data integration from heterogeneous organized & unorganized channels of feedback (Call Center + Emails + Social Media, mobile apps, online blogs & portals, online repositories) Enable actionable & reliable insights for civic agencies & residents through truthful descriptive & prescriptive analysis 17 Xerox Confidential Cityzen Urban Sensing Platform
- 18. 18 November 21, 2016 Thank You archana.ramakrishnan@xerox.com Xerox Internal Use Only
- 19. A single and open platform for a global understanding of mobility FORECASTIN G CONTROL VISUALIZATION MODELLING SIMULATION MOBILITY ANALYTICS PLATFORM DIAGNOSTIC S IN DATA GATEWA Y IN DATA GATEWA Y OUT DATA GATEWA Y DATA & BIZ INTELLIGENCE CONSUMERS IN DATA GATEWA Y DEMOGRAPHIC S WEATHER GIS SOCIAL NETWORKS SENSOR NETWORKS SERVICES DATA PUBLIC TRANSPORT TOLLING PARKING VALUE-ADD PARTNERS External DATA TRANSPORTATIO N AUTHORITIES (CUSTOMERS) CITIZENS PARTNERS LOCAL ECONOMY STARTUPS UNIVERSITIES 19
- 20. An interactive visual data analytics tool • Editing screen for parameters • View with static or dynamic heat map • Analysis of any metrics • Fare validations, sales, ticket inspections, passenger counts,… • Time filters • Select a period – Day X to Day Y • Choice of frame frequency for dynamic heat map • Data filters • Operators • Modes of transport • Routes or group of lines • Fare products • Customer profile • Benefits November 21, 201620 Focu s
- 21. Schedule adherence analysis • Feature : • Combines vehicle load estimation and vehicle trip tracking data into rich visualizations • Benefits : • Identify actual bottlenecks in the network • Advantages : • Spatio temporal understanding of schedule deviation • Consider the actual impact for the passengers • Continuously up to date Page 21
Hinweis der Redaktion
- The focus of my presentation is around how technology can help develop supporting ecosystems and thereby improve bus services, taking reference from some of the work that Xerox has been doing with city transit authorities.
- Talking point : Growth of Indian cities, rapid urbanization, more cars on the road leading to less efficient public transit
- Talking point : Working with government and public transit authorities, launch of the app, powered by Xerox with integrated payment mechanisms and an underlying analytics platform to encourage transit operators to make efficient use of the private transport modes, reduce private car ownership , while improving their overall efficiency. Why this makes sense : Enables better last mile connectivity. Better understanding of mobility patterns and becoming more demand responsive.
- Key messages: This vision is implemented through our product called Mobility Analytics Platform So far it has been commercialized with features targeting analytics of Public transport and Off-street parking leveraging our platforms We are working on other aspects of transportations (Next Gen Mobility through GODENVER/LA data and road traffic through UMTRI connected vehicles) The platform is now ready to be commercialized as a standalone offering on top of any data sources available to the customers
- Source of data : Public transport Ticket validations In most of the case only check-in data. Alighting is estimated by our algorithms. When Check in-check out data is available no estimation is required to construct the same view. About the inferences done by algorithms: Inference of passenger alighting location The algorithm builds a statistical model of the likely alighting location of each regular user based on several assumptions: the symmetry of daily travels. Usually, in the morning you leave your home to go to work. You validate your card, and the system retrieves a log. But we don’t know where you get off the bus. At the end of the day, usually you go back to your home We also take into account the reproducibility of travel behavior : each Monday, at noon you play tennis, each Wednesday you pick up your children from the nanny … Finally we assume that the travelers with a single trip ticket will follow the same behaviors Automatic clustering of network into zones Dynamic zoning is obtained by aggregating elements in OD matrices by their similarities in two complementary aspects, travel demand and geo-location. They form a two-view representation of the problem; this permits us to adapt one of multi-view clustering methods, namely multi-view spectral clustering.
- Source of data : Public transport Ticket validations In most of the case only check-in data. Alighting is estimated by our algorithms. When Check in-check out data is available no estimation is required to construct the same view. About the inferences done by algorithms: Inference of passenger alighting location: same as OD analysis Vehicle Trip reconstruction merging fare and schedule data We cope with the case when both information on bus ridership and schedules is available but no correspondence is established. Reasons why bus trips and schedules are available but not the assignment are several. In most cases, they come from different sources. Schedules are known in advance for one or multiple service seasons, while bus ridership is collected by vehicle tracking systems, on the daily basis. The correspondence is manually established by the operator and therefore is a subject of multiple omissions or errors. We address the problem of finding the optimal correspondence between real bus trips and schedules. We cope with the correspondence ambiguity when multiple matching choices may be possible on both sides. With this task being the combinatorial optimization problem, we develop an efficient matching algorithm. We process schedules and real trips which, like any real traffic data, are a subject of all kinds of traffic delays, missed trips, multiple types of service on the same line, additional services, etc.
- Sources of data: Vehicle(bus tram , train, …) schedules Either from public schedules (e.g. GTFS) Either from real vehicle trips recorded by the CAD-AVL systems like ORBCAD Strong diffrentiator: Use of our Xerox Trip planner technology enables almost instant computation of 1 to all destinations from any schedule information.
- While the rest of the data analysis have been using ticket data, there is another ecosystem that is often overlooked, but equally important source of information for improving bus services. Typically surveys are created to gather insights on customer satisfaction. But in the new millennial age, social media is bound to play a greater role in the way public transit agencies communicate with their customers. Towards this goal, Cityzen is a tool that XIG has developed
- What it
- Key messages: Ultimate objective is to use transactions collected from transportation services together with various sources of open data in order to reconstruct a complete view of mobility patterns in an area From this reconstructed view we can add algorithms and tools that help city planners to visualize, control, diagnose, predict and simulate mobility Our primary target users are transportation authorities but this can benefit the whole ecosystem
- For any metrics being analyzed: The system allows views on MAPs (static or dynamic) and detailed graphs The views are highly interactive and configurable This enables a quick identification of salient information from the mass of data
- Sources of data: Vehicle(bus tram , train, …) schedules adherence data recorded by the CAD-AVL systems like ORBCAD Key innovation: Can be combined with vehicle load reconstruction in slide 6 for joint analyis of load and late/early events