Bus Bridging Assessment Tool and Visualization Dashboard Alaa Itani, MASc. Olufunbi Disu-Sule iCity – ORF Webinar, June 2020
Outline ▪ Research Team ▪ DASh-Bus: A Decision Support Toolkit – Use Cases • Bus Bridging Assessment Scenario • Bus Bridging Optimization ▪ Visualization Dashboard 2
Research Team ▪ Visualization Dashboard ▪ DASh-Bus Conceptualization and by OCADU Development by University of Toronto – Olufunbi Disu-Sule – Alaa Itani – Dr. Greice Mariano – Dr. Aya Aboudina – Prof. Jeremy Bowes – Dr. Siva Srikukenthiran – Prof. Ehab Diab – Prof. Amer Shalaby 3
DASh-Bus: A Decision Support Toolkit
Background A Total of 6,500 70% of the 144 unplanned buses were requested buses subway closures in requested were from 2015 operational bus routes Economic cost of m ajor subway passengers’ delay in New York City ~ $389 million annually 5 DASh-Bus
Background Major unexpected rail Often, a simplistic disruptions occur approach is followed for frequently selecting shuttle buses Can lead to extensive Result in degraded delays for passengers service and potential loss and buildup at stations of loyal passengers DASh-Bus
Objectives Develop a tool to help agencies Provide measures of the impact on Provide measure of how well dispatch shuttle buses and train and bus passengers shuttle buses are used evaluate different scenarios DASh-Bus
Methodology Overview Route C Route A Route D Route E Route B DASh-Bus
Methodology Overview (Cont.) Waiting and travel time Shuttle buses tracking Shuttle Buses serving based on shuttle service rail passengers End of incident and Time for dissipating return of buses passengers queue DASh-Bus
Use Cases ✓ Bus Bridging Scenario Assessment ➔ DASh-Bus Planner ✓ Bus Bridging Optimization ➔ DASh-Bus Optimizer 10
Use Case #1: Bus Bridging Scenario Assessment 11
Data Input and Output Subway Passengers’ Delay Incident location and time Bus Riders’ Delay Expected duration of incident Detailed measures at disrupted stations Dispatch time and Demand reduction Longest queue at disrupted stations DASh-Bus Number & assignment of shuttle buses Planner Detailed impact on each bus route Transit network characteristics Shuttle buses performance measures Train and bus ridership Degree of utilization of shuttle buses Train and bus travel time Deadhead time of shuttle buses DASh-Bus
Case Study: Assessing an Existing Bus Bridging Plan Disruption occurred during the morning peak period lasting for 31 min Closing 6 stations , between Bloor-Yonge and Eglinton 1,729 Eglinton North Bound 18 Davisville 94 Platforms South Bound 5.7 bus-hr. Platforms St. Clair 77 57 wasted time 22.5 7 Summerhill 20 Rosdale 5 734 Bloor-Yonge Delays at the Disrupted Subway Stations Couldn't Serve Served (Passenger-hr.) 13 DASh-Bus
Testing Other Response Plans 1 Eliminate non-utilized buses Deadhead Time Unused Buses 70 60 50 Minutes 40 30 20 10 0 48 83 26 11 24 73 137 91 72 125 87 75 69 86 24 24 44 44 82 29 43 63 103 Bus Route • 51% reduction in bus users’ delay • Zero-min Wasted Time 14 DASh-Bus
Testing Other Response Plans (Cont.) 2 Dispatch shuttle buses from nearby routes Shuttle Buses Performance Metrics 16.0 14.0 12.0 Average (Bus-hr.) Change in Users Delays (Prs-hr) 10.0 100 8.0 31 50 Subway Riders’ Delay 6.0 0 Bus Riders’ Delay -50 4.0 -100 2.0 -150 0.0 -200 Bus out-of-original- Buses dead-head time Bus-on-shuttle-service Buses 'waste' time -250 service time time -300 Sc0: Baseline Scenario Sc5: Different Routes -350 -400 -395 -450 15 DASh-Bus
Use Case #2: Bus Bridging Optimization 16
Data Input and Output Incident location and time Expected duration of incident Dispatch time and demand reduction Number of shuttle buses DASh-Bus Number & assignment of shuttle buses Optimal Bus routes Optimizer Number of buses from each route Transit network characteristics Initial end station for each bus Train and bus ridership Train and bus travel time DASh-Bus
Evolutionary Algorithm Nexus Simulation Platform New Generation of Web User Interface Chromosomes Mutation Initial Parent Solution Set Selection: Roulette wheel Cross Over DASh-Bus No (User Delays) Mapping of Most Fit Yes chromosome Fitness Stop? Chromosome: representation Evaluation Lowest Delay into UDMT Bus Rider Subway input Delays Rider Delay Average Fitness of all chromosomes forming a plateau 18 DASh-Bus
Comparison of Outcomes Current Plan Optimal Plan Average Deadhead Time 18 16 9% 14 Deadhead time 12 48% Minutes On-shuttle Time 10 Current Plan 52% Optimal Plan 8 6 91% 4 2 0 15 mins, on average, is saved in deadhead Optimal plan shows a better utilization of time of each shuttle bus shuttle buses along the disrupted segment 19 DASh-Bus
Publications ▪ Aboudina, A., Itani, A., Diab, E., Srikukenthiran, S., and Shalaby, A. (in press). Evaluation of bus bridging scenarios for railway service disruption management: a users delay modelling tool. Public Transport . DOI: 10.1007/s12469-020-00238-w. ▪ Itani, A., S. Srikukenthiran and A. Shalaby, 2020. “ Capacity-Constrained Bus Bridging Optimization Framework ”, Transportation Research Record. ▪ Itani, I., A. Aboudina, E. Diab, S. Srikukenthiran and A. Shalaby, 2019. “ Managing Unplanned Rail Disruptions: Policy Implications and Guidelines towards an Effective Bus Bridging Strategy ”, Transportation Research Record, Vol. 2673(4), pp. 473-489. ▪ Diab, E., G. Feng and A. Shalaby, 2018. “ Breaking into Emergency Shuttle Service: Aspects and Impacts of Retracting Buses from Existing Scheduled Bus Services ”, Canadian Journal of Civil Engineering, Vol. 45(8), pp. 647 -658. 20 DASh-Bus
Initial Visualization Dashboard 21 DASh-Bus
Visualization Dashboard
What was missing from the initial dashboard prototype? ▪ Visualizations of several scenarios simultaneously ▪ Graphically scaled passenger counts ▪ Side by side comparison of data and map ▪ Delay time for arriving passengers at affected stations ▪ Complete overview of system ▪ Interactive data visualizations ▪ Distinct visualizations of unique trends and data sets ▪ No potential for real time vehicle tracking 23
First Iteration 24
Assessment ▪ Display delay using unique graphics ▪ Lacking in any comparative statistical data ▪ Compare two different scenarios ▪ Display surrounding bus lines ▪ Increase levels of interactivity ▪ Support decision making ▪ Improve map readability 25
Second Iteration 26
Assessment ▪ Provide overview of entire scenario ▪ Display total user delay for each scenario ▪ Increase meaningful data displayed ▪ Further increase the interactivity of the dashboard ▪ Necessary for a display of 2 scenarios simultaneously ▪ Map elements should have tooltips and dialogue boxes when selected/hovered. 27
Third Iteration 28
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Acknowledgments ▪ ORF ▪ Trapeze Inc ▪ NSERC ▪ OCE ▪ SOSCIP DASh-Bus 32
Questions? 33
Potential Use Cases ▪ Key challenges that face transit agencies post-COVID – “ Improving the management of flows to avoid crowds/excessive concentration of travelers in a given place at a given time ” ~ Sylvain Haon, Senior Director Strategy at UITP ▪ Relief of overcrowding using shuttle buses – “ Imposing physical distancing in public transport vehicles means operating them using only 20 per cent of their capacity ” ▪ Managing rail disruptions post-COVID – “ It’s equivalent of only 8 to 10 passengers in standard bus… ” Source: Intelligent Transport. “Looking Ahead to Public Transport Post - Pandemic.” Accessed June 5, 2020. https://www.intelligenttransport.com/transport-articles/100389/looking-ahead-to-public-transport-post-pandemic/. 34 DASh-Bus
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