KEY PERFORMANCE INDICATORS FOR URBAN ITS
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1 KEY PERFORMANCE INDICATORS FOR URBAN ITS I. Kaparias, M. Bell Imperial College London N. Eden, A. Gal-Tzur Technion Israel Institute of Technology A. Tsakarestos, M. Gerstenberger Technische Universität München Brussels, 11 October 2011
2 Aims and objectives Main output of CONDUITS: Coherent set of Key Performance Indicators (KPIs) for urban traffic management and ITS Objectives to define KPIs evaluation framework for identifying best practices and technologies to test the KPIs through application to Paris, Rome, Tel Aviv and Munich
3 Roles of KPIs Assess benefits Cost vs. benefit of investment Assess the usefulness of ITS as a whole Identify the limits of ITS Assist traffic managers to make decisions Contract monitoring Promote cities interests
4 Selection criteria for KPIs Make use of existing data, not collect new Measurability (costs, tools, accuracy) Easy to use and communicate to public Clarity (general public, policy makers, professionals) Consider individuality of cities Geographical scale (local-national-international) Control (ability to change characteristics)
5 Aspects of KPIs Mode red times at signalized junctions Exposure to congestion % of vehicles arriving to stations on time Spatial City-centre travel times Temporal Last 15 min travel times Suburban freeways travel times % trips % trips non Year obligated obligated Annual % trips return home 44.0
6 Aspects of KPIs Perspective Travel Times Modal split Tram delays Level of responsibility Gap time from call to repair Tolling system uptime Categories Distance to stations Travel times Number of fatalities
7 KPIs framework (1) Goal : Improve attractiveness of public transport Objective : Reduce public transport waiting times in junctions PI : Average waiting time at stop line PI : % of vehicles stopping at stop line Objective : Improve reliability of public transport PI : Variance of headway between consecutive vehicles at the station PI : % of vehicles arriving at the station on time Performance Measure : Vehicle s momentary location
8 KPIs framework (2)
9 Case studies Paris Implementation of a priority system to 3 bus lines Construction of a new tram line Rome General assessment of traffic efficiency Tel Aviv Implementation of new signal strategies Munich Application of speed feedback signs for drivers
10 Applied KPIs Paris Traffic efficiency: mobility for buses and tram Traffic safety: accidents for buses and tram Rome Traffic efficiency: mobility, reliability Tel Aviv Traffic efficiency: reliability Munich Traffic safety: direct impacts
11 Paris Bus priority (1) Priority on lines 26, 91, 96 Implementation in 2006 Anticipated travel time savings of the order of 30s per traveller on average, i.e. saving of one vehicle
12 Paris Bus priority (2) Supplied data Bus travel times on a number of specific segments of given length on the 3 bus lines, before and after Vehicle traffic speeds on a number of specific segments of given length, affected by the priority measures on the 3 bus lines, before and after Casualty numbers due to road traffic accidents on a number of specific segments affected by the priority measures on bus line 91, over given periods before and after Vehicle traffic flows on the given segments, before and after
13 Paris Bus priority (3) Traffic efficiency: Mobility index minutes/km, weighted for public and private transport Traffic safety: Accidents index casualties per million vehicles, weighted by severity
14 Paris Bus priority (4) Traffic efficiency: Mobility index Separately for public and private transport min/km Line 26 Line 91 Line 96 TOTAL Public transport mobility Private transport mobility Combined, with wpt = 0.7, wpv = 0.3 min/km Line 26 Line 91 Line 96 TOTAL IMOB
15 Paris Bus priority (5) Traffic safety: Accidents index For each severity level Line 91 Weight Cycles wheelers wheelers 0.15 Pedestrians 0.4 Casualties/million-vehicles Deaths Serious injuries Slight injuries Total, with wdead = 0.85, wser = 0.1, wsl = 0.05 casualties/ million-veh Line 91 IACD
16 Paris Tramway (1) Construction of tramway line T3 in 2006 at Boulevards des Maréchaux It was anticipated to achieve the following goals: Average speed of 20km/h Daily traffic of 100,000 travellers Regularity of the line with a tram every 4 mins
17 Paris Tramway (2) Supplied data Tram travel times on the entire route, only after Vehicle traffic speeds on the entire route of the tram, before and after Casualty numbers due to road traffic accidents on the entire route, over given periods before and after Vehicle traffic flows on the entire route, before and after
18 Paris Tramway (3) Traffic efficiency: Mobility index Separately for public and private transport min/km Public transport mobility Private transport mobility N/A Tram T3 Combined, with wpt = 0.7, wpv = 0.3 min/km Tram T3 IMOB N/A 3.70
19 Paris Tramway (4) Traffic safety: Accidents index For each severity level Tram T3 Weight Cycles wheelers wheelers 0.15 Pedestrians 0.4 Casualties/million-vehicles Deaths Serious injuries Slight injuries Total, with wdead = 0.85, wser = 0.1, wsl = 0.05 casualties/ million-veh Tram T3 IACD
20 Rome General assessment (1) Several techniques and technologies, including ITS, are used for traffic management in the entire Greater Rome area Mobility Control Centre
21 Rome General assessment (2) Supplied data Travel times by private and public transport between all zones in the city, along with route lengths Occurrences of congestion and their average duration on a number of key routes on the road network over one year
22 Rome General assessment (3) Traffic efficiency: Mobility index minutes/km, weighted for public and private transport Traffic efficiency: Reliability index dimensionless, weighted by link and mode
23 Rome General assessment (4) Traffic efficiency: Mobility index Separately for public and private transport min/km Rome Public transport mobility Private transport mobility N/A 5.41 N/A 3.20 Combined, with wpt = 0.7, wpv = 0.3 min/km Rome IMOB N/A 4.75
24 Rome General assessment (5) Traffic efficiency: Reliability index Routes weighted equally (assumption) IREL =
25 Tel Aviv New signal strategies (1) Municipality incharge of mobility within the borders of the city of Tel Aviv Avivim Traffic management & Control System
26 Tel Aviv New signal strategies (2) Recurrent Congestion During the noon / Evening peak ~45 Hour of Congestion/Link/Month Deployment of new traffic management strategies
27 Tel Aviv New signal strategies (3) Supplied data Level of Service (LOS) of links in Ha Shalom arterial, along with link lengths and weights Duration of congestion of links in Ha Shalom arterial during the afternoon peak period Weighting methodology Timeframes: 5 timeframes to reflect the typical traffic demand patterns Link categories: arterials, local streets Direction categories: inbound, outbound (into and out of the city centre)
28 Tel Aviv New signal strategies (4) Traffic efficiency: Reliability index Weights Inbound Outbound Morning Peak noon Peak Off Peak Morning Peak noon Peak Off Peak Arterials Local streets The new strategies were implemented during the afternoon peak
29 Tel Aviv New signal strategies (5) Results: Comparing the index during afternoon peak hours 2 months before and 2 after the implementation of the new signal programmes: average increase of 36% in the reliability index Bigger decrease in congestion duration (~41%) General perception of representative travellers supported this figure Decreasing tendency of the index value stopped over the next few months; within one year the index value became stable
30 Munich Safety assessment (1) Installation of two feedback signs during a test period Measuring speeds at two urban streets (speed limit 50 and 30km/h) in both driving directions Shown flashing messages: if exceeding the speed threshold slow down!, else Thank you!
31 Munich Safety assessment (2) Supplied data Time and speed of each vehicle passing the location Daily traffic volume and the number of vehicles exceeding the speed limit Data available before implementation, during test period and after implementation of the feedback signs
32 Munich Safety assessment (3) Traffic Safety: direct safety impact number of shown warning messages/day average number for each time period: before implementation, during test period and after implementation
33 Munich Safety assessment (4) Traffic Safety: direct safety impact warnings/vehicle Test period 1 Paosostrasse (east) Paosostrasse (west) Friedenspromenade (north) Friedenspromenade (south) Combined, with wl1 = 0.2, wl2 = 0.2, wl3 = 0.3, wl4 = 0.3 IDS warnings/vehicle Munich (Paosostrasse & Friedenspromenade) Test period
34 Concluding remarks Implementation of KPIs requires consideration of several dimensions KPIs developed proved to reflect major phenomena KPIs as a predictive tool?
35 Thank you! For further enquiries:
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