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Mobile Transit Planning with Real Time Data Jerald Jariyasunant, Dan Work, Branko Kerkez, Eric Mai Systems Engineering Program, Dept. of Civil and Environmental.

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Presentation on theme: "Mobile Transit Planning with Real Time Data Jerald Jariyasunant, Dan Work, Branko Kerkez, Eric Mai Systems Engineering Program, Dept. of Civil and Environmental."— Presentation transcript:

1 Mobile Transit Planning with Real Time Data Jerald Jariyasunant, Dan Work, Branko Kerkez, Eric Mai Systems Engineering Program, Dept. of Civil and Environmental Engineering Center For Information Technology Research in the Interest of Society (CITRIS) UC Berkeley

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5 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

6 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

7 Current trip planning tools for mass transit Schedule based trip planners exist Real-time bus arrival information exists

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10 Current trip planning tools for mass transit Schedule based trip planners exist Real-time bus arrival information exists

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13 Problem of schedule adherence percentage of vehicles that run on time according to schedule within (-1min, +4 min) –San Francisco area ~ 70% Random passenger arrivals at bus stops cause buses to bunch, and deviate from the schedule. –Inherent to the system! Hypothesis: Real time data is vital for trip planning [San Francisco Municipal Transportation Authority, 2008; Metropolitan Transportation Authority; Pilachowski and Daganzo, 09]

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15 Problem of schedule adherence percentage of vehicles that run on time according to schedule within (-1min, +4 min) –San Francisco area ~ 70% Random passenger arrivals at bus stops cause buses to bunch, and deviate from the schedule. –Inherent to the system! Hypothesis: Real time data is vital for trip planning [San Francisco Municipal Transportation Authority, 2008; Metropolitan Transportation Authority; Pilachowski and Daganzo, 09]

16 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

17 System architecture

18 Needs from 3 rd Party Providers Static Data Route Configuration Data Schedule Data Dynamic Data Estimated Bus Arrivals to Stops

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21 Needs from 3 rd Party Providers Static Data Route Configuration Data Schedule Data Dynamic Data Estimated Bus Arrivals to Stops

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25 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

26 The graph

27 The Challenge Transit trip planners based only on schedules have an easy problem – just find the shortest path. Can be achieved with Dijkstra’s algorithm, A-Star, etc. With real-time data: Numerous ways to get from point A to point B Based on the location of buses, running early or behind schedule, the fastest path is always changing

28 The graph

29 The Challenge Transit trip planners based only on schedules have an easy problem – just find the shortest path. Can be achieved with Dijkstra’s algorithm, A-Star, etc. With real-time data: Numerous ways to get from point A to point B Based on the location of buses, running early or behind schedule, the fastest path is always changing

30 The graph

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32 Each lookup from the third party server costs time Cannot lookup wait/travel time of every link 3

33 Feasible Paths Take advantage of the fact that we are finding the shortest path in a transit network Create database of feasible path from Origin of every route to Destination of every route (San Francisco Muni = 87 x 87 table) Feasible paths: Any possible path that takes 3 transfers or less Remove any path that includes more routes than any other feasible path Heuristic works for San Francisco Database contains X paths between bus routes

34 Example Network A B C DE F GH

35 Built Database ABCDEFG Aroute list B C D E F G List of feasible routes for each pair (includes location of transfer points)

36 Real Time Lookup A B C DE F GH

37 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

38 Experiment Description 2 random points in San Francisco were generated Various paths from point A to point B were determined by a schedule-based planner and a real-time trip planner and then compared Buses were then tracked to find out the actual trip times between point A and point B along various paths

39 Example Comparison StepRouteDirectionIntersectionLatitudeLongitudeStop ID 128Inbound19th Ave & Quintara St37.74853-122.475916 228Inbound19th Ave & Irving St37.76343-122.4770424 329OutboundLincoln Way & 19th Ave37.765526-122.4776131 429OutboundLincoln Way & 27th Ave37.765131-122.48613635 (Schedule Based) (Real-Time) (Actual) StepTime of SegmentCumulativeTime of SegmentCumulativeTime of SegmentCumulative 125 36 38 26316424 363710521052 42392542 Total Trip time Schedule Based:44.65min Real-Time:59.65min Actual:59.91min

40 NextBus Arrival Estimates Next bus arrival estimates are noisy Median error is small (+/- 1 min) They outliers are problematic –Need to be identified and filtered, or custom arrival estimator needed

41 Real-Time Planner vs. Schedule Based Planner Accurate Prediction Planner overestimated trip time Planner under- estimated trip time Real-Time Mean:.91 StdDev:.19 Schedule Based Mean:.85 StdDev:.20 Ratio of estimated tt over actual tt Percentage of trips

42 Real-time data is useful Statistically significant? Result of Student-t test shows means are different Result of Wilcoxon signed-rank test shows medians are different Both CI > 99.99%

43 Limited reliability with current estimation

44 Additional Facts Compared single fastest path suggested by both transit trip planners with the ground truth (found by tracking buses) Schedule based planner 15% of trips missed transfer (wait for next bus) Predicted actual fastest path in 46% cases Real-time planner 9% of trips missed transfer (wait for next bus) Predicted actual fastest path in 53% cases

45 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

46 What’s next Improved travel time estimation –Need to model the dynamics of the network to use the real time data more intelligently Studies of travel behavior

47 Mobile Transit Planning with Real Time Data Jerry Jariyasunaut Branko Kerkez Dan Work Systems Engineering Program, Dept. of Civil and Environmental Engineering Center For Information Technology Research in the Interest of Society (CITRIS) UC Berkeley Questions Q

48 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

49 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

50 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

51 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

52 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

53 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

54 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

55 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

56 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

57 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

58 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

59 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

60 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

61 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

62 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

63 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

64 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

65 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

66 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

67 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

68 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

69 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

70 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

71 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

72 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

73 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

74 Outline Motivation System architecture Routing with real-time data System analysis Mobile client implementation Next steps

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