2013 ITS-NY TWENTIETH ANNUAL MEETING June 13-14, 2013; Saratoga Springs, NY The Past, Present, and Future of ITS AGENDA

Transcription

1 2013 ITS-NY TWENTIETH ANNUAL MEETING June 13-14, 2013; Saratoga Springs, NY The Past, Present, and Future of ITS AGENDA Friday, June 14, :30 Panel 4: Emerging ITS Applications Panel Moderator: Arthur O Connor, USDOT/FHWA New York Metro Office I-94 Connected Vehicle Truck Parking System, Steve Sprouffske, Kapsch New York City s ITS Deployment Status, Dr. Mohamad Talas, New York City Department of Transportation Request-based Multi-modal Traffic Signal Control with Connected Vehicles, Dr. Qing He, State University of NY at Buffalo The Challenges in Asking and Answering the Right Questions about ITS Impacts on Travelers, Dr. John Falcocchio, Polytechnic Institute of New York University

2 Kapsch TrafficCom Improving Commercial Vehicle Truck Parking Steve Sprouffske Manager, ITS Solutions and Pre-Sales Titel der Präsentation Untertitel der Präsentation 1

3 Kapsch TrafficCom The Truck Parking Problem 8 th on ATRI s Top 10 industry concerns Safety and Compliance issue FHWA disbursed $34M in discretionary funding ATRI Truck Parking Survey Polled 242 drivers and 93 carriers most important factors over the shortage of truck parking fatigue management compliance with hours of service regulations Driver fatality when a Nissan Sentra struck the right rear of a tractor-trailer parked on the left shoulder of the entrance ramp to the Interstate 95 southbound rest area in Fairfield, Conn. late Wednesday Dec 12, php#ixzz2UDFwXHjN 2

4 Kapsch TrafficCom I-94 Truck Parking Project Goals Enhance highway safety by providing timely and reliable truck parking information Provide a sustainable and scalable truck parking solution Provide a secure solution that protects user privacy and data Maximize user acceptance of the system for truck parking decisions Leverage connected vehicle concepts for expanding information exchange 3

5 Kapsch TrafficCom Stakeholders and Partners 4

6 Kapsch TrafficCom The V2X Landscape 5

7 Kapsch TrafficCom Expanding information flow A truly connected vehicle is being realized 5.9 GHz DSRC, LTE/cellular, and even 915MHz all provide a mechanism for getting information to the driver and road operator Each technology decision has advantages and disadvantages Where does the cost reside? Connectivity options Bandwidth Ability to integrate with other technologies Technology selection should provide a solution that meets the needs of the end-user 6

8 Kapsch TrafficCom What is V2X? V2X is the combination of services utilizing bi-directional communications between vehicles and the roadside. Vehicle-to-vehicle (V2V) and vehicle-to- infrastructure (V2I) communication are the core communication paths utilized within the connected vehicle environment. Safety and mobility services fall into one of these categories. V2V focuses on those services which provide safety of life and property between vehicles. V2V extends the safety envelope around the vehicle. V2I focuses on those services which utilize roadside infrastructure to provide not only safety services but also mobility improvement services. 7

9 Kapsch TrafficCom System Concept MDOT will disseminate information over multiple channels: Dynamic truck parking signs MiDrive Website Connected Vehicle application ParkingCarma Smartphone Apps 8

10 Kapsch TrafficCom Corridor Overview Up to 20 public and private truck stops and rest areas 9

11 Kapsch TrafficCom System Architecture 10

12 Kapsch TrafficCom I-94 Truck Parking Project Overview 5 Sites equipped with 5.9 transceivers Integration with Michigan traffic management system Data collated from public and private parking facilities Data provided to truck drivers on a tablet 11

13 Kapsch TrafficCom Driver HMI 5.9 data displayed on 7 Android tablet Parking data displayed based on driver direction of travel Only relevant information displayed Mile Marker/Exit number Truck stop affiliation Parking space availability Trending information Data age 12

14 Kapsch TrafficCom Why 5.9 GHz DSRC? Open standards (technology public not owned by a single company) Banking grade security of over the air data Device anonymity Adaptable to medium and short range communication zones provide the best of both worlds: Communication range minimum of 3000 ft. with clear line of sight Exact Lane-Level Localization at the transaction point Multi-service enforcement Tolling, ORT, HOT CMV E-Screening Fixed and mobile enforcement 13

15 Kapsch TrafficCom 5.9 Project Components Kapsch TrafficCom 5.9 Products Roadside products In-vehicle products MTX/TRX-9450 TS

16 Kapsch TrafficCom Solution Multiplicity Expands Benefits to Commercial Vehicle Market V2X enables relevant/time sensitive data for weigh station clearance and driver information Driver identification Vehicle condition data Hours of service Traffic management/road conditions V2X integrates with other roadside sensors (WIM/ALPR/USDOT Reader) providing complete vehicle information The system will identify key information for officers to intelligently and quickly identify trucks with faults. Fleets can save upwards of a billion US dollars annually through the use of electronic fleet credentials which can be stored on the V2X transponder. A single stop at a weigh station will cost a driver or fleet over $8 and 1 gallon of fuel 15

17 Kapsch TrafficCom Thank You! Steve Sprouffske Manager, ITS Solutions & Pre-Sales (760) Please Note: The content of this presentation is the intellectual property of Kapsch and all rights are reserved with respect to the copying, reproduction, alteration, utilization, disclosure or transfer of such content to third parties. The foregoing is strictly prohibited without the prior written authorization of Kapsch TrafficCom IVHS Inc. Product and company names may be registered brand names or protected trademarks of third parties and are only used herein for the sake of clarification and to the advantage of the respective legal owner without the intention of infringing proprietary rights Titel der Präsentation Untertitel der Präsentation 16

18 Kapsch TrafficCom Back up slides Titel der Präsentation Untertitel der Präsentation 17

19 Kapsch TrafficCom Road-side Solution Product name Description Special features MTX/9450-E Transceiver platform for Connected Vehicle Safety and mobility V2I applications Precise localisation, stand-alone operation, dual Ethernet, internal or external antennas selectable, built-in GPS RX, IEEE p, IEEE 1609 compliant 18

20 Kapsch TrafficCom TS GHz ITS On Board Unit Key Features Characteristics Target Applications References Compatible with IEEE p, IEEE 1609.x and SAE J2735 Compatible to current specifications of ITS G5 Supports IEEE based security for signing and verification Optional external antenna connector for WAVE/ITS G5 Bluetooth interface to smart phone, tablet or laptop. Built-in GPS receiver Single PCB design with integrated 5.9 WAVE/ITS G5, GNSS and Bluetooth solutions. No internal connectors. Operational temperature range: -40 C to +85 C Internal re-chargeable battery tested up to +105 C. Output power: +14 dbm Receiver Sensitivity: -90 dbm (6 Mbps) Commercial Vehicle Inspection Electronic Payment and Access Control Transit Signal Priority Traveler Information Signal Phase and Timing USDOT Safety Pilot applications CAM/DENM NYSDOT Schodack FTE / Sun Pass US DOT Safety Pilot ACS Help Testfeld Telematik / ITS WC

21 New York City ITS Deployment Current Update And Future Plans Mohamad Talas, PE. PTOE. PhD New York City Department of Transportation

22 Vision for NYC DOT Sustainable Streets: NYCDOT s Strategic Plan Improve Safety and Mobility Modernize ITS 2

23 NYC Traffic Control TMC in Queens (Long Island City East of the 59 th St. Bridge) - intersection management & NYPD Administration NY City has ~12,400 Signalized Intersections Circa : VTCS central real-time active control ~6100 Manhattan: ~2,700 Outer boroughs: ~3400 Leased telephone lines: >$7M annually ~4000 intersections 2007/Present: Traffic Signal Computerization Expansion (9200 intersections) & NYC Wireless NYCWIN Past: Long-held favored Manhattan-bound in the AM/PM outbound Current: Active Traffic Management & Adaptive Control. Slide 3

24 ITS Equipment Deployment SYSTEM INVENTORY City And State DOTs Highway ITS Deployment 130 mile VMS 111 CCTV 500 Traffic Signals: (Computerized) 9,000 HAR 9 DETECTORS 1000 RFID READERS 150 City Fiber Network 100 mile 4

25 FDR ITS, NYC ITS Projects EAST RIVER BRIDGES 9A AND HENRY HUDSON BQE, LIE, BELT, VAN WYCK, GCP CROSS BRONX, MAJOR DEAGAN, BRUCKNER EXPRESSWAY, HUTCHENSON RIVER PARKWAY Bus Rapid Transit MIM Adaptive control Smart Light Slide 5

26 NYCDOT Modernize ITS NYC Traffic Control System (TCS) to replace VTCS ASTC NTCIP field communications Traffic Responsive TSP oriented Real-time data collection Dynamic split adjustment Adaptive Traffic Control Slide 6

27 Advanced Solid-State Traffic Controllers Slide 7

28 Midtown in Motion (MIM) MIM An off shoot of the ITS Modernization project Study Area -- Midtown Manhattan Powerhouse of NYC Initial Zone: 2 Ave to 6Ave, 42St to 57St Slide 8

29 Project Challenges and Approach Dense CBD Oversaturated, congested Low vehicle speeds (< 10 mph) Need reliable data to manage and assess performance Need a centralized system to monitor and control 9

30 Occupancy (%) Microwave Sensors ITS Design Use both flow & occupancy Given flow different occupancies, different flow conditions Concept of regimes Define level of congestion using both measures Congested Free Flowing Flow (vphpl) Slide 10

31 Microwave Sensors Mid block location ideal ITS Design Microwave Sensor Location Slide 11

32 ETC Tag Readers ITS Design Unique environment with high penetration of ETC tags Strategic placement Maximize coverage 2 readers per location Reliable source of travel time Works under congested conditions and slow vehicle speeds Slide 12

33 Sensor Network Deployed ETC Tag Readers Travel Time 23 readers Microwave Sensors Flow and Occupancy 100 sensors Cameras Field Conditions 25 IP cameras Slide 13

34 Control Policy For an extremely complex grid network, developed following approach Hierarchical Control Level 1 Strategic area wide control Implemented by Avenue Rebalance traffic being delivered to the zone Use library of carefully developed plans Level 2 Tactical control Implemented at intersection level Complimentary to level 1 Balance queueing and minimize gridlock condition Slide 14

35 Time (Seconds) Level 1 Control Plan Library Design Madison Avenue - Green Window, Pre-Existing vs NBP Zone Existing New Pre-Existing Start of Window Pre-Existing End of Window NBP Start of Window NBP End of Window Intersections Direction of Traffic Develop library of plans and field test Define trigger condition based on real time data Slide 15

36 Travel Time (sec/veh) Relation of Travel Time to # of Stops Stops Display of Control Regimes of ETC Tag Reader Based Travel Times Stops Stops Stop 0 12:00:00 AM 2:24:00 AM 4:48:00 AM 7:12:00 AM 9:36:00 AM 12:00:00 PM 2:24:00 PM 4:48:00 PM 7:12:00 PM 9:36:00 PM 12:00:00 AM Solid line = Median Slide 16

37 Level 1 Control Define trigger conditions based on real time data Travel Time Area Wide Control Plan 2 Stops Network Balancing Plan (NBP) 3 Stops Advanced Control Plan (AC1) 3+ Stops Advanced Control Plan (AC2) NBP Simultaneous offset, minimal green tapering AC1 Simultaneous offset, increased green tapering AC2 Simultaneous offset, higher green tapering Slide 17

38 Occupancy (%) Park Ave LEXINGTON AV Lex Ave 3rd AV Level 2 Control E 59th ST Measure of queuing condition, relative to block length Estimate queue using flow/occupancy and E E 5558th ST then calculate Severity Index Threshold: Q U Threshold: Q M 100% Flow, Occupancy, Severity Index Representative Sample for Avenues Block Length: L Threshold: Q L 90% 80% 70% E 57th ST E 54 th St 60% 50% 40% 30% 20% 10% SI=1 Q < L/3 SI=2 L/3 < Q < 2L/3 SI=3 2L/3 < Q < 3L/4 0% Flow per lane (vphpl) SI=4 Q > 3L/4 Slide 18

39 Percent of Time New Metrics Relative Distribution of SI 60% 50% 40% 30% 20% 10% 0% 1 4 Cross Street SI Avenue SI 4 1 Average SI Avg. of SI by approach by interval Equity Ratio Avg. SI (Cross St) / Avg. SI (Ave) 19

40 MIM In Action REAL TIME DATA ANALYSIS + RECOMMENDATIONS REVIEW FIELD CONDITIONS TAKE ACTIONS BASED ON REVIEW Slide 20

41 Level of System Usage Travel Time Calendar Format Coffee Spill plot Metrics Queue length and Severity Index Relative Distribution Average SI Equity Ratio 21

42 Travel Time (sec/veh) Level of Activity Stops AC2 Display of Control Regimes of ETC Tag Reader Based Travel Times AC2 700 AC1 AC1 600 CTOD NBP CTOD Stops AC Stops NBP Stop CTOD 0 12:00:00 AM 2:24:00 AM 4:48:00 AM 7:12:00 AM 9:36:00 AM 12:00:00 PM 2:24:00 PM 4:48:00 PM 7:12:00 PM 9:36:00 PM 12:00:00 AM Slide 22

43 Level of Activity Madison Avenue Sample of control decisions Mon 3/5/ AM 10-12PM 12-1PM 2-4PM :00 AM 10:00 AM 12:00 PM 2:00 PM 4:00 PM 6:00 PM 8:00 PM 4-6PM NBP 6-8PM AC1 AC2 23

44 Travel Time History over Time Median travel time across months, shown above Seasonal demand, Special events, incidents, etc. trigger some congestion despite ATM Care must be exercised in before/after evaluation Slide 24

45 Results to date Phase A Completed August 2011 Initial results show an improvement of speeds around 10% within the study area The overall speeds considering both inside study area and approaching study area were comparable Level 2 control, has been helping to reduce queuing (and in turn gridlock) while achieving equity Slide 25

46 Results to Date Comparison of average speed AM = 8AM to 10AM, MD = 11AM to 1PM, PM = 4PM to 6PM Highlighted cells for improved speed AM MD PM 6th Avenue 5th Avenue Madison Avenue Lexington Avenue 3rd Avenue Before After Before After Before After Before After Before After Zone Outside Overall Zone Outside Overall Zone Outside Overall Slide 26

47 Planned Expansion Next Steps Phase B will be live shortly Phase C underway Slide 27

48 Data Sharing NYCDOT developed a tool to view travel time data NYCDOT will share information with app developers Slide 28

49 Real Time Flow Map Slide 29

50 Lessons Learned Cost effective solution, built on existing ITS Rapid deployment of ITS in record time ITS design adapted to conditions Mid block vehicle sensors Optimized ETC tag readers locations Centralized control and monitoring Integrate technologies in operations ATM makes recommendations Operator review using cameras Decision making and action Slide 30

51 Lessons Learned Reliable data source for real time control Travel time data using ETC tag readers Flow and occupancy from microwave sensors Robust metrics for real time control Median travel time for control decisions Severity Index Hierarchical control is very effective to minimize gridlock and congestion Strategic Level 1 Tactical Level 2 Slide 31

52 Features of Select Bus Service (SBS) Bus lanes Branding Faster fare collection SBS Passenger info Bus signal priority Stations 32

53 Features of Select Bus Service (SBS) 33

54 SBS Results Speed: 15-20% faster Ridership: 5-10% increase in first year Customer Satisfaction: over 95% satisfied or very satisfied Safety: 1 st and 2 nd Avenues saw a 21% reduction in traffic injuries in sections with full design treatments Cost Efficiency: Projects range from ~$5M-$20M, large FTA share Bx12 M15 M34/34A S

55 Future ITS Deployment Brooklyn Bridge ITS East River Bridges ATM Jackie Robinson Parkway Belt Parkway Korean Veteran Parkway Smart Light Deployment Data Management Slide 35

56 Future ITS Data Management Data Characteristics and Opportunity Diversity of data types Travel Time, Flow, Occupancy, Taxi GPS, Passenger Trips Environmental Impact data Monitoring Objective: Improve the quality of life by managing the existing network and improve future Management and planning for NYC to become more productive, livable, equitable, and resilient Slide 36

57 Future ITS Data Management DEMAND VOLUME INDICATES AMOUNT BY WHICH DEMAND > CAPACITY OCCUPANCY Travel Time 37

58 Potential For ATDM Data Reliable data source for real time Data Travel time data using ETC tag readers Flow and occupancy from microwave sensors NYC BRT Project GPS Taxi GPS Congestion metrics for real time Congestion Index as function of (Travel Time, flow rate, Ocupancey etc) Slide 38

59 Research In Progress Region Wide University Research Center Interface NYU POLY NYU CUSP UTRC CUNY MIT Objective: information interface between the TMC real time data and research center for analysis and potential feedback and improvement. Slide 39

60 Strategies In Progress Information Dissemination Regional: TRANSCOM NYC Centers interface with Transportation Partners Interface with Public, Web, Video to Media, Mobil Apps and Connected Vehicle Program Slide 40

61 Participant Q&A Slide 41

62 Request-based Multi-modal Traffic Signal Control with Connected Vehicles Qing He University at Buffalo, The State University of New York June 14, ITS-NY Twentieth Annual Meeting Saratoga Springs, NY

63 Outline Background: Definitions, Challenges and Assumptions. A Request Responsive Model for Multi-modal Traffic Signal Control Interviews from traffic control professionals Numerical Examples Conclusions

64 What is Multi-modal Traffic Signal Control? Multi-modal control: provide a comprehensive traffic information framework to service all modes of transportation. Multiple request control: Reduce travel disutility from multiple requests in the same traffic modes Rail, pedestrian, and passenger cars Background Multiple buses and multiple passenger cars Signal Control Modeling

65 Multi-Modal Intelligent Traffic Signal Systems (MMITSS) Background Signal Control Modeling Multi-Modal Intelligent Traffic Signal Systems (MMITSS) is considered as one of the Dynamic Mobility Applications (DMA), a U.S. DOT research program. ( A MMITSS system consists of following components as a bundle (USDOT RITA 2013) Intelligent Traffic Signal System (ISIG) Transit Signal Priority (TSP) Mobile Accessible Pedestrian Signal System (PED-SIG) Emergency Vehicle Preemption (PREEMPT) Freight Signal Priority (FSP)

66 Major Challenges in Multi-Modal Signal Control (He et al. 2010,2011,2012,2013) 1. Signal timing optimization with competing modes First-come first-serve is not true. 2. Signal coordination with multi-modal control Signal coordination is probably disrupted by priority control. 3. Actuated signal control with multi-modal control How to make use of existing traffic detectors to reduce delay for both passenger cars and priority vehicles. Background Signal Control Modeling

67 Assumptions in This Work Background Signal Control Modeling Assumptions: 1.Vehicle-to-Infrastructure communications are available for priority modes (emergency vehicles, buses, pedestrians) Connected Vehicles (known as VII or IntelliDrive SM ) 2.Background coordination signal plan is available Cycle length and offset are offline/online optimized by other signal optimization tools. 3.The sequence of phases is fixed Avoid confusion to the motorist

68 Overview of Signal Control Modeling (1) 1. Modeling actuated signal control: Green time = necessary green + max green extension time. Priority requests Passenger vehicles g' pk epk - Necessary green (similar as minimal green) time for phase p during cycle k - Maximal available green extension time for actuated control at phase p during cycle k Signal Control Modeling Interviews

69 Overview of Signal Control Modeling (2) 2. Request-based multi-modal traffic control Priority requests from E.V., buses, pedestrians and so on. Signal Control Modeling Coordination requests for signal coordination along an arterial. Interviews

70 Overview of Signal Control Modeling (3) Signal Control Modeling Interviews 3. Modeling passenger car delay Area below queue profile = passenger car delay Total passenger vehicle delay at phase p cycle k: D V pk p k S E Qpk Qpk Qp, k 1 Qpk [( )( g' pk epk) ( )( t' p, k 1 ( t' p, 2 2 S E k g' pk e pk ))] p, k

71 Signal Control Modeling Objective Interviews Under saturated : - Minimize total weighted delay Min P V c wmjpd D D mjp pk pk ( m, j, p) k p k p P k p c e pk wmjp priority weights for mode m, jth request on phase p Oversaturated : -Minimize weighted delay, also maximize passenger car throughput Min P V c wmjpd H D mjp pk pk ( m, j, p) k p k p P k p c e pk V S H pk Qpk pk( g' pk epk) Qpk p, k E

72 Multi-modal Signal Control : Learning from Professionals Interviews Numerical Examples Subjects: 7 Buffalo police officers and 1 fireman, with average 14 years of experiences Interviews: Please assign a prioritization weight (1-10, 10 is most prioritized, 1 is least) for different traffic modes. Emergency vehicles > Large size of pedestrians > High occupancy bus > truck trailer (if block any entrance/exit) > passenger cars

73 Simulation Settings with VISSIM Consider three travel modes: Buses, pedestrians and passenger cars Four bus routes (high freq: 3 min/bus, low freq: 10min/bus) Bus communication range: 650 feet Eight pedestrian crosswalks Pedestrian communication range: 50 feet Four volume scenarios (scenario 1-3: bus only. scenario 4: bus and pedestrian) Ten simulation runs with different random seeds for each scenario Numeric Examples Conclusions

74 Performance Comparisons Performance comparisons Robus priority coord. : proposed approach ASC-TSP coord : Transit Signal Priority with coordination ASC coord : Coordination only Limitations of TSP: 1. Serve one bus at a time 2. First come first serve Numeric Examples Conclusions 13

75 Results of Multi-modal Delay Numeric Examples Conclusions Pass. car delay: -4% Bus delay: -25% Ped. delay: -14%

76 Conclusions Conclusions IBM Transportation Projects Proposed multi-modal signal control policy is able to Resolve conflicting multi-modal priority requests Provide signal coordination by requests Response to vehicle real-time actuations Proposed method Robust-priority coord. outperforms state-of-practice ASC-TSP coord Small passenger car delay reduction (4%) Medium pedestrian delay reduction (14%) Large bus delay reduction, especially with high frequency buses (25%)

77 Thanks! Questions and Comments? References He, Q., K. L. Head and J. Ding, PAMSCOD: Platoon-based Multi-modal Traffic Signal Control with Online Data, Transportation Research Part C, Volume 20, Issue 1, February 2012, pp He, Q., K. L. Head and J. Ding, Heuristic Algorithm for Priority Traffic Signal Control, Transportation Research Record: Journal of the Transportation Research Board, No. 2259, 2011, pp. 1 7 He, Q., K. L. Head and J. Ding, Multi-Modal Traffic Priority Signal Control with Connected Vehicles", submitted, 2013 He, Q., Robust-Intelligent Traffic Signal Control within a Vehicle- to- Infrastructure and Vehicle-to-Vehicle Environment, PhD Dissertation, University of Arizona, 2010

78 BACKUP

79 Location: Six real-world intersections in Anthem, Phoenix, AZ Controller: Econolite ASC/3 Test vehicles: 3 AZ REACT (Regional Emergency Action Coordinating Team ) vehicles Field Tests

80 We found.. DSRC max communication range: [700, 1100] meters NTCIP hold and force-off control can override the existing signal plan. The code worked in real-time in embedded Linux systems. The formulation was solved for both concurrent and conflicting requests.

81 The Challenges of Asking the Right Questions About ITS 20 th Annual Meeting of ITS-NY Saratoga Springs, NY June 14, 2013 John Falcocchio; NYU-POLY 1

82 Purpose To show that while travelers and communities are the most important customers of ITS deployment, there is a lack of metrics on ITS benefits that focus on travelers and communities John Falcocchio; NYU-POLY 2

83 Transportation Engineers Typically address transportation problems by focusing on four technical areas impacted by ITS: Physical infrastructure Technology Vehicles Operating systems John Falcocchio; NYU-POLY 3

84 Technical Areas Require metrics that quantify the output of a network or facility such as: Network speed Reliability Throughput Capacity Schedule adherence John Falcocchio; NYU-POLY 4

85 However The effectiveness of ITS solutions to transportation problems is primarily determined by how they benefit travelers and communities John Falcocchio; NYU-POLY 5

86 BUT Public Transportation Agencies tend to ignore the fact that the CUSTOMER is the central focus of their business This is a major institutional issue John Falcocchio; NYU-POLY 6

87 Need to Ask the Right Questions What are the benefits that travelers and communities receive from ITS deployment? How do we measure these benefits? John Falcocchio; NYU-POLY 7

88 Traveler Issues Require metrics that quantify the benefits of the project on travelers (e.g.): real-time information mobility convenience comfort John Falcocchio; NYU-POLY 8

89 Community Issues Quality of life aspects impacted by transportation John Falcocchio; NYU-POLY 9

90 Some ITS Benefits are Difficult to Measure Not everything that counts can be counted (William Bruce Cameron) Clearly additional research is needed to overcome this challenge John Falcocchio; NYU-POLY 10

91 Real-time Information What is the value of real-time information to a driver caught in traffic congestion? to someone waiting for a bus or train late at night? Or at any other time? John Falcocchio; NYU-POLY 11

92 How Should we Measure the Benefit of Real-time Information? In 1992, after implementing the VMS on the highly congested Ring Road in Paris it was found that when drivers were informed of the travel time to their exit fewer drivers exited the congested expressway to use local streets John Falcocchio; NYU-POLY 12

93 In A Congested CBD Network Increasing average vehicle speed by 1-2 mph does not have a measurable impact on the individual traveler But it may have significant cumulative health benefits on drivers, pedestrians and others exposed to the pollutants emitted John Falcocchio; NYU-POLY 13

94 Institutional Leadership ITS evaluation criteria that include travelers and community issues should be established by top management John Falcocchio; NYU-POLY 14

95 At the Project Planning Phase Require metrics for travelers and community conditions before ITS deployment John Falcocchio; NYU-POLY 15

96 WHY is this a Critical Issue? Failure to adequately document pre-its conditions: reduces the public s understanding of ITS benefits for travelers and communities and, contributes to placing ITS projects at a lower priority than traditional capital projects John Falcocchio; NYU-POLY 16

97 Thank You John Falcocchio; NYU-POLY 17