Impact of the North South Line Project Dr. Ties Brands Malvika Dixit 27 th November 2018 1
North-South metro line in Amsterdam 2
Time for a Quiz! kahoot.it 3
Travel time from Centraal Station to Station Zuid by NZL 4
Daily ridership for the NZL Approx. 90,000 on average Workday Approx. 78,000 on average Saturday 5
Busiest station of the NZL 30,000 Boarding+alighting per station and week day 25,000 20,000 15,000 10,000 5,000 0 Station Zuid Europaplein De Pijp Vijzelgracht Rokin Centraal Station Noorderpark Noord Working day Saturday Sunday 6
Busiest section of the NZL 25000 20000 Load per period, average working day Load per period, average working day 25000 20000 15000 15000 10000 10000 5000 5000 0 0 AM peak Off-peak PM peak AM peak Off-peak PM peak 7
Busiest hour of day on a Workday (based on check-in time) 10000 Travellers per hour of day 9000 8000 7000 6000 5000 4000 3000 2000 1000 0 5 to 6 6 to 7 7 to 8 8 to 9 9 to 10 10 to 11 11 to 12 12 to 13 13 to 14 14 to 15 15 to 16 16 to 17 17 to 18 18 to 19 19 to 20 20 to 21 21 to 22 22 to 23 23 to 24 24 to 25 8
Busiest hour of day on a Saturday (based on check-in time) 7000 Travellers per hour of day 6000 5000 4000 3000 2000 1000 0 5 to 6 6 to 7 7 to 8 8 to 9 9 to 10 10 to 11 11 to 12 12 to 13 13 to 14 14 to 15 15 to 16 16 to 17 17 to 18 18 to 19 19 to 20 20 to 21 21 to 22 22 to 23 23 to 24 24 to 25 9
To and from which tram line do people transfer the most? Share of transferring passengers at station Vijzelgracht 3% 29% 37% 31% Line 1 Line 7 Line 19 Line 24 10
Impact of the North South Line Project plan 11
Project themes Public transport Mobility and accessibility Public space and liveability Spatial economics 12
Main research questions TU Delft / AMS How does PT supply change due to NZL? How and by who is the NZL used? What are the effects of the surrounding PT network? How is accessibility perceived in the Amsterdam region and does this change due to the NZL? 13
Project Research overview framework Data analysis Travel surveys Passenger volumes Travel times Number of transfers Network flows Reliability Accessibility Compare, explain, model, predict Travel times Reliability Accessibility Crowding Access / egress Passenger volumes Travel times Number of transfers Network flows Reliability Accessibility Travel times Reliability Accessibility Crowding Access / egress Before opening July 22nd 2018 After opening Objective quality perceived quality 3 14
Data sources GTFS timetable data (open source) Smart card data From GVB (~800,000 records per day) From other operators in the city region Combined data: TransLink Systems application AVL data From GVB and NDOV Survey inhabitants of Amsterdam ~3.800 respondents Travel time perceptions of last trip GPS tracking app 15
Vehicle km (Thousands) Vehicle hours Preliminary Results Impact on Vehicle km & hours for Amsterdam Vehicle km (Average Weekday) Vehicle hours (Average Weekday) 113 110 3,752 3,585 51 46 37 36 55 49 2,313 2,284 2,140 2,213 1,327 1,227 15 19 469 563 Bus Tram Metro Bus Bus GVB CXX EBS Bus Tram Metro Bus Bus GVB CXX EBS Before NZ Line After NZ Line Before NZ Line After NZ Line *Source: scheduled time table from General transit Feed Specification (GTFS) data Before period : 28 th May - 1 st July 2018 After period : 10 th Sept - 14 th October 2018 16
Expected results For OD pairs: Average travel time Travel time variability (reliability indicator) Number of transfers Translation to societal benefits For stop-stop segments: Crowding in vehicles Usage of the NZL: Time of day Day of the week Season Holidays Transfer to and from the NZL Type of traveller (based on fare type) 17
Expected results Differences actual and perceived service quality Development and validation of route choice models, with attributes like travel time (in-vehicle, waiting, transfer) travel costs number and type of transfers, transfer environment reliability indicator crowding indicator (on route level) individual characteristics /fare card type Accessibility analysis and equity impact 18
Impact of the North South Line Reliability Indicator 19
Project Research overview framework Data analysis Travel surveys Passenger volumes Travel times Number of transfers Network flows Reliability Accessibility Compare, explain, model, predict Travel times Reliability Accessibility Crowding Access / egress Passenger volumes Travel times Number of transfers Network flows Reliability Accessibility Travel times Reliability Accessibility Crowding Access / egress Before opening July 22nd 2018 After opening Objective quality perceived quality 3 20
What is Reliability? Certainty of service aspects compared to the schedule as perceived by the user Reliability of travel time Regularity Punctuality 21
Context Urban transit networks typically multi-modal Reliability based on the whole journey experience including the transfers Two leg journey with two PT modes Waiting time In-vehicle time Mode 1 Transfer time Waiting time In-vehicle time Mode 2 22
Objective To develop a metric that measures reliability for multi-modal public transport journeys; is sensitive to all travel time components; and enables the comparison between different transit modes and routes. To use the metric for before/after comparison of the North-South metro line 23
Data Sources Smartcard data Tap-in and tap-out location and times Automatic Vehicle Location (AVL) data Vehicle number, stop location and time stamps 24
Methodology - RBT Reliability buffer time (RBT): Difference between the 95 th and 50 th percentile travel time experienced by the passengers on a stop-stop (o-d) pair using a particular route (r)* RBT o,d,r = tt o,d,r o,d,r 95 tt 50 Interpreted as the additional time passengers have to budget for their travel to ensure on-time arrival one out of twenty times *Route : A combination of public transport services a passenger may choose, where each route may or may not include a transfer. 25
RBT using smartcard data Where first tap-in at station (For eg. metro) Total travel time (t 5 -t 0 ) Where first tap-in inside vehicle (For eg. bus/tram) Total travel time minus waiting time at origin (t 5 -t 1 ) Waiting time In-vehicle time Mode 1 Transfer time Waiting time In-vehicle time Mode 2 t 0 t 1 t 2 t 3 t 4 t 5 26
Travel time reliability for multi-modal journeys RBT calculated for each stop-stop (OD) pair and route RBT o,d,r = tt o,d,r o,d,r 95 tt 50 Weighted average calculated for each mode/line/stop 27
Application to GVB Network March 2018 (before NZ line) Approx. 800,000 smartcard transactions/day Two weekdays used for analysis 28
Results Reliability per Mode Mode(s) used Metro (+ Metro-Metro) Number of journeys Unimodal Journeys Median travel time (mins)* RBT (mins)* 235,287 14.7 5.9 Tram 315,410 15.4 6.6 Bus 104,495 14.8 6.2 Tram-Tram 1,755 23.2 7.2 Bus-Bus 130 20.5 9.1 Multimodal Journeys Metro-Tram 7,588 25.0 7.6 Metro-Bus 747 28.8 7.8 Tram-Metro 6,665 26.3 8.3 Bus-Metro 1,336 28.7 8.5 *Calculated for two weekdays (1 st and 2 nd March 2018) for all day (7am to 7pm) 29
Results Reliability of accessing transit hubs 30
Reliability by Route Used - Station Sloterdijk to Boelelaan 31
Reliability by Route Used - Station Sloterdijk to Boelelaan 32
Conclusion New metric proposed for travel time reliability measurement considering multimodal journeys including waiting and transfer times for all legs of the journey comparable across modes Demonstrated application to (sample) Amsterdam data but can be applied to other networks Provides reliability at a very disaggregate level flexibility of aggregation (eg. mode, transit stop and route level) 33
Limitations and Future Work Assumed that passengers boarded the first vehicle Did not consider the impacts of availability of real-time information on passenger arrivals and their waiting time distribution Low sample size (only two days) to be applied to a months data for before/after comparison 34
Questions & Discussion 35
Policy relevant research questions Related to public transportation or other research themes? Short term: PT network operation? Long term: infrastructure planning? Ex-post / exante policy evaluation? 36
Thank you! T.Brands@tudelft.nl M.Dixit-1@tudelft.nl 37