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Incident-Management In Central Arkansas Federal-aid Project Number: ITSR(001)

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Presentation on theme: "Incident-Management In Central Arkansas Federal-aid Project Number: ITSR(001)"— Presentation transcript:

1 Incident-Management In Central Arkansas Federal-aid Project Number: ITSR(001)

2 2 Motorists An Integrated and Shared System Incident System Operators

3 3 Incident Management Activities Motorist Assistance Patrol –3 vehicles operating on I-30, I-40, I-630, I-430, and I-440 in the urbanized area. –Proposed to provide some coverage of both US 67/167 and I-530, from I- 30 to Dixon Road Towing and Wrecker Service –A rotation list of qualified towing and wrecker services. –Current procedures do not specify a minimum response time. Emergency Medical Services (EMS) –911 calls –Communications upgrades are needed. Traffic Management at Work Zones –Queue detectors –Variable message signs (VMS) and highway advisory radio (HAR) Traveler Information System –511 calls

4 4 Goals of Our Study Model the distribution of incidents. Investigate advanced incident detection techniques Choose the appropriate incident-response strategies Perform Benefit/Cost (B/C) analysis

5 5

6 6 Incident Data of Arkansas Arkansas State Police Report (2000 ~ 2003) rural or urbantype of collisions weekdayslight conditions roadway alignment roadway profile weatheralcohol involvement crash severity road systemcountieslarger municipalities Frequency

7 7 Assistant Parts Incidents Others GPS/GIS VC# Programming Internal Information System TransCAD Server GISDK Script Programming Update Map SQL Database Output PlatformsWeb Application ASP.NET programming Core: 1. Planning Model 2. Operating Model Architecture of Software System

8 8 SIMAN Dynamic shortest path EMS fleet assignment & demand coverage MRM (Multicriteria Routing Module) DRA (Dynamic Routing Algorithm) SIMANI (Stochastic Incident-Management of Asymmetric Network-Workload – Integrated)

9 9 Incident Management Model Functions 1. Provide a good tactic to allocate available response vehicles to serve reported incidents. 2. Pay attention to potential incidents in ensuring a certain level of reliability in delivering quality service. 3. The model helps to reduce the negative impact of incidents as much as possible. Algorithm SIMAN

10 10 Potential workload at f = f(1) v(2) 2 1 Reported & potential Incidents Risk = 20% Workload = 3×20 min Potential workload at v=20 Delay at f = 80 min

11 11 Comparison between Rotation and SIMAN RotationSIMAN Total Number of Vehicle Dispatches 66,757 Total Delay Cost (veh-min)259,787, ,343, Mean of Work Time (min) Standard Deviation of Work Time (min)

12 12 Multicriteria Routing Module

13 13 Operational Model – Dynamic Routing CBA Intermediat e Starting Arrival 2035

14 14 Arkansas Crash Data for 2003 FatalInjuryPDOFatalitiesInjuries 55728,12542, ,474

15 15 Users Motorists Operators Managersc xv Environment Travel Time Incident s System Data Input & Analysis Core Algorithms Output Platforms Functional Structure of the Prototype Incident Command Center

16 16 Technical Partners (in alphabetical order) Gary Dalporto, Joseph Heflin, & Sandra Otto, FHWA Scott Bennett, Mark Bradley, Marc Maurer & Alan Meadors, AHTD Karen Bonds, AR State Police David Taylor & Brian Nation, Arkansas Department of Health and Human Services Casey Covington, Minh Le, Richard Magee, & Jim McKenzie, Metroplan Bill Henry & Jerry Simpson, City of Little Rock Doug Babb, Routh Towing Service

17 17 Key Team Members Gregory Browning Yupo Chan Isabel Farrel Adeyemi Fowe Jian Hu Heath McKoin Weihua Xiao Ildeniz Yayla

18 18 Publications Hu, J. and Chan, Y., “A Multi-criteria Routing Model for Incident Management,” Proceedings of the IEEE International Conference on SMC, Sept. 2005, Hawaii, pp Hu, J. and Chan, Y., “Stochastic Incident-Management of Asymmetrical Network-Workloads,” TRB Pre-print , 85th Annual Meeting of the Transportation Research Board, Washington D.C. January 22-26, Hu, J. and Chan, Y. "A Dynamic Shortest-Path Algorithm for Continuous Arc Travel-Times: Implication for Traffic Incident Management.” Pre-print , 87th Annual Meeting of the Transportation Research Board, Washington D.C. January 13-17, Hu, J. and Chan, Y. "Dynamic Routing To Minimize Travel Time And Incident Risks", Paper No. 485, 10th International Conference on Applications of Advanced Technologies in Transportation, Athens, Greece, 27-30, May, 2008.

19 19 Thanks. Any Question?

20 20 Planning & Operational Models Functions 1. Provide incident managers with best strategies to respond to an incident 2. Assist motorists on re-routing around incidents, and incident response operators on dispatching response vehicles. Two Algorithms 1. Multi-Criteria Optimization (as a planning tool): Paper: Hu, J. and Chan, Y. (2005), “A Multi-criteria Routing Model for Incident Management,” Proceedings of the IEEE International Conference on SMC, Sept., Hawaii, pp Dynamic Routing (as an operational tool): Paper: Hu, J. and Chan, Y. (2006), “Dynamic Routing to Minimize Travel Times and Incident Risks,” Accepted for presentation and Proceedings of the 9th International Conference on the Application of Advanced Technologies in Transportation, ASCE, Chicago, IL, August, 2006.

21 21 Schedule of Incident Service Dispatch time Incident Occurrence Incident Notified Response Unit Assignment Response Unit Arrives at Scene Detection time Response vehicle travel time Incident clear time Work time Response time Incident Restoration

22 22 Planning Model – Multicriteria Optimization Four Criteria Distance 1) Distance It has implications on operating cost, including oil price. Travel Time: 2) Travel Time: It is tied to operating cost and response time. Variance in Travel Time:. 3) Variance in Travel Time: It measures the travel time reliability. Risk Index:. 4) Risk Index: Risk exposure is an indicator of highway safety. Objective Function Minimize Wt 1 × Tour Dist + Wt 2 × Travel Time + Wt 3 × Var + Wt 4 × Risk Indx NOTE: 1) Weight 1 + Weight 2 + Weight 3 + Weight 4 = 1 2) The model yields all viable (dominant) routings for all weights.

23 23 Dominant Tours Wt. Set TourOriginal TourDist. Expected Time Time VarianceRisk Index

24 24 Path Dist, Time, Var & Risk on Network NODES

25 25 An Example

26 26 Objective function Delay Cost For each incident in the network Delay Cost = Cost × Delay Cost = Traffic Volume (Vehicle) Delay = Work Time (Minute) fixed costs for dispatching response vehicles = Number of response vehicles × Unit cost to dispatch a vehicle

27 f(1) v(2) 2 1 Incident parameters λZ1Z1 Z2Z2 Z3Z3 Z4Z4 Z5Z5 f(1) v(2) Df DvDf Dv ×202×203×204×205×20 W f =3×20 C f =80 C v =100 J f =70 J v =40 H=20 K=5

28 28 Node v(2) Node f(1) f(1) v(2) 2 1 Incident workload Rt0t0 t1t1 t2t2 t3t3 t4t ∞∞∞ Rt0t0 t1t1 t2t2 t3t3 t4t ∞∞∞ W f =3×20 C f =80 C v =100 J f =70 J v =40 H=20 K=5

29 29 Operational Model – Dynamic Routing Improved Feature: 1) Time-dependent travel time 2) Measuring of Incident Risk using Poisson Processes and Queuing Theory 3) Allowing waiting at nodes along the path 4) Incident risks are combined into the shortest path algorithm as paroxysmal delays, which are incorporated as part of the travel time.

30 30 ATIS Architecture

31 31 Work in Progress: Incident Detection

32 32 Functional ICC/TMC

33 33 Administrative Remarks UALR is upgrading equipment (as additional matching) Your guidance is necessary in designing the Software architecture Need More Information: 1) Time-dependent Travel Time for each Highways 2) Details on the current practice in servicing an incident 3) Information on the available Towing Truck Companies


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