Risk assessment and incident impact traffic flow estimation techniques for a monitor integrated road safety system.

Transcription

1 Risk assessment and incident impact traffic flow estimation techniques for a monitor integrated road safety system. Kyriacos C. Mouskos, PhD, Director of research, CTL Cyprus Transport Logistics Ltd Athanasios K. Ziliaskopoulos, PhD, Associate Professor, University of Thessaly Evangelos A. Mitsakis, Dipl. Civil Eng., Hellenic Institute of Transport Centre for Research and Technology Hellas Tommaso Bonino, Dipl.Ing, SRM Bologna Public Transport Agency Curtis Barrett, VISTA Transport Group Inc. Abstract This paper is concerned with off-line risk assessment and incident impact traffic flow estimation techniques that can be integrated within advanced safety monitoring systems of urban road networks. Data requirements, design methodology and prototype developments are presented and discussed. The research has been conducted within the framework of the EU-funded FP6 MISS Monitor Integrated Safety System project. Under the MISS project a Dynamic Traffic Assignment (DTA) model that is embedded in the Visual Interactive System for Transport Algorithms (VISTA) software was implemented as a prototype model for the Province of Bologna (PROBO) Italy and the Nicosia, Cyprus networks to estimate the traffic conditions for the entire network and to estimate the impacts of incidents (construction, accidents, roadway deficiencies, other) on traffic flow conditions and to evaluate route diversion strategies. The DTA model produces dynamic user equilibrium traffic flow characteristics at the network, subnetwork level, path level, and link level. A Risk-Assessment Algorithm was developed through a combination of historical accident records for PROBO and stated preference questionnaires of bus and transport professionals. The MISS-RAA utilized VISTA to normalize the accident data based on the estimated traffic flow rates. The MISS-RAA produced a prototype GIS risk map for each link of the network. Introduction Research framework description The MISS ( project aims to develop an innovative platform that will dynamically sense and predict natural and infrastructure conditions, in order to improve safety and efficiency of transport operations in multi-environmental scenarios. The project s outcomes are going to increase citizens and operators safety by enabling a just-in-time intelligent computation of an open dynamic road surveillance network and streamlining alerting tasks under the daily duty provided by clerical staff. In this way MISS will pay attention to viability of the system in the long term. The MISS platform uses advanced communication technologies for on-line services as well as high capacity storage devices for off-line services and applications, in addition to new advanced algorithms that simulate risk assessment and incident impacts. The MISS adaptable platform is a mobile system explicitly designed to monitor rough environmental and infrastructure conditions and it will be integrated with any pre-existing legacy system. It enables intelligent exchange of structured information between the operational fleet vehicles and the Unified Operative Centre (UOC) where information will be elaborated and actions planned.

2 The platform is composed by two main components: An UOC where data coming from a monitoring system that is comprised of a set of mobile and fixed distributed devices is stored and analyzed by an innovative simulation algorithm to find a strategy to improve the end-users safety and security and to avoid traffic congestion. An innovative on-board kit: it will be installed on the fleet cars and will include a black box here named MSCU (MISS Storage & Communication Unit), where raw environmental and infrastructure data will be stored and elaborated; then, these data will be sent via a radio-communication network to the UOC. Risk Assessment Algorithm (MISS-RAA) model The Province of Bologna (PROBO) maintains a VISUM model of the provincial road network. This model has been used to extract the Origin-Destination (OD) matrices between the centroids of the network zones and thus the corresponding OD pairs. The extracted data, in form of ASCII files together with the topology of the road network in GIS shapefile format has been imported into the PostgreSQL database of VISTA (Visual Interactive System for Transportation Algorithms). VISTA is a network-enabled framework that integrates spatio-temporal data and models for transport applications. The strength of VISTA is the ability to use DTA and Dynamic User Equilibrium (DUE) algorithms with the integrated RouteSim simulation module. The OD pairs of the PROBO network are and the corresponding zones are 337. The network contains links with lengths varying between 0,012km and 21,63km. The total demand of the network is vehicles. Fig. 1 The VISUM model - PROBO Fig. 2 The VISTA model in the Java Client After the import of the data into VISTA the Cell Generation module has been used in order to transform the network of links into a network of cells. The model used for this procedure is the Cell Transmission Model. Cells are defined such that the length of a cell is equal to the distance travelled by a single vehicle at the defined free flow speed. The Demand profiler module has been in order to specify the percentage of delay in the delay table to implement during a subsequent run of DTA and Simulation. After the run of the two above mentioned modules VISTA has been capable to execute the DTA - Path Generation module. In this module, DTA has been computed with a calculation of the time-dependent shortest paths at each iteration. This allowed the available path set to update throughout the DTA process. Since the process is simulation-based, the RouteSim simulator has been automatically called within the module.

3 The DTA - DUE module has computed the DTA without the calculation of time-dependent shortest paths at each iteration. It redistributed demand among the existing path sets. The calibration of the network model has been based on the rather few (almost ten) actual traffic counts that the Province of Bologna has carried out. It is noted here that the main objective was the development of a prototype model rather than an operational model. An operational model would require the conduct of network wide traffic counts, an updated OD survey and travel time studies that are outside of the scope of MISS. After this procedure the PostgreSQL command interface has enabled the extraction of traffic flows (number of vehicles) for each road link. The SQL command that has been used for the extraction of the traffic flows is: select max (timestep) as timestep,max (flow) as flow from vf_flowout_tdd( 'base', 'unique id of link' ) The result is the number of vehicles for any link of the network. Thus, the risk level could then be computed as a risk index, based on the accidents which occurred in each road link as follows: Risk index = Number of accidents/link length/car density in the link Fig. 3 Final risk index map of the Province of Bologna road network. Data unavailability and the development of expert opinion stated preference questionnaires In order to enhance the accuracy of the final risk assessment results, two stated preference questionnaires, the first regarding the computation of risk for bus operations and the second the risk for the entire network, have been compiled and filled by experts of ATC (public transport authority of Bologna) and PROBO traffic experts respectively. The questionnaires aim to gather opinions of local traffic experts, who are aware of the regional road network conditions and the risks that are associated with transport operations. Stated preference questionnaire for bus operations The stated preference questionnaire regarding the bus operations consists of 64 multiple choice questions, which are derived from a compilation of various conditions related to weather, day time and road type traffic situations that occur in bus lanes or may have an impact on bus lanes operations such as a vehicle blocking the bus lane partly or fully with alarm lights on or off, an obstacle blocking the bus lane partly or fully and a construction zone partly or fully existing in

4 the bus lane. The answers that can be chosen by the experts have been selected as an applicable countermeasure (action) that should be taken for each described situation. The countermeasure actions are the immediate towing, towing but not urgent, ticketing, warning or ignoring. Stated preference questionnaire for the entire network The questionnaire consists of the first part (A) that includes 14 multiple choice questions plus a second part (B) with one extra question which requires a free text answer by each expert and a third part (C) with a free text question which aims to point out the exact hazardous locations of the Province of Bologna road network by name followed by an explanation. The first part with the 14 multiple choice questions have been put together by combining various conditions and situations related to weather, time of day, urban type of roadway, road type, number of lanes, shoulder, pavement, roadway geometry, traffic flow, traffic speed, truck presence, pedestrian movements and abnormal events. The variables included in the questionnaire were based on a combination of the variables that are being recorded for each accident in the PROBO accident database, the variables that are not included in the PROBO accident database but have significant contribution to accidents according to the literature review, and the specified functionalities of the MISS on-board unit. Fig. 4 Draft implementation of the Risk Assessment Algorithm. Incident Impact Traffic Flow Estimation System The incident impact traffic flow estimation system utilizes a DTA model based on the VISTA software. Given the characteristics of the incident such as location, capacity reduction, incident duration, roadway capacity under ideal conditions, and prevailing traffic flow characteristics (historical Origin-Destination (OD) matrix, traffic counts, prevailing roadway speed), it provides an estimation of the existing and prediction of future traffic conditions. Over the past years the transportation planners utilized static traffic assignment (STA) models in predicting the route choices of the travelers due to limitations in the state of the art and computational power that was necessary to run large-scale transportation networks. However, during the past decade, the transportation profession has witnessed a revolution and the first

5 few DTA models are now available. The United States Federal Highway Administration (FHWA) ( has sponsored the development of two models, DYNASMART ( and DYNAMIT ( that were developed by Mahmassani et.al. (1993), Daganzo (1994), initially at the University of Texas at Austin (now at the University of Maryland) and by Ben-Akiva et.al. (1994) at the Massachusetts Institute of Technology, respectively. Independently, Ziliaskopoulos (2000) at Northwestern University developed another model called the Visual Interactive System for Transport Algorithms DTA (VISTA - model (Ziliaskopoulos and Waller, 2000). A review of DTA models can be found in Peeta and Ziliaskopoulos (2001). The development of DTA models substantially enlarges the scope of transportation related studies and bridges the differences between traffic operations and transportation planning studies. The main characteristic of DTA is that it produces the Dynamic User Equilibrium (DUE) At equilibrium no user can switch path and improve either his departure or his arrival time - timespace trajectory of each individual vehicle from its origin to its destination. Each vehicle trajectory includes the departure time from the origin, the arrival time at the destination, the vehicle s chosen path and the location of the vehicle at any time of interest along this path. For a transit or intermodal traveler, a DTA model produces the corresponding time-space trajectory that may involve two or more modes of transportation. The current implementation of DTA models is restricted to off-line studies, however it is envisioned to be used as an on-line trafficforecasting tool to support traffic operations and traveler information systems as more computationally efficient software/hardware and algorithms are designed. It is worthwhile to note the similarities and differences between DTA models, Static Traffic Assignment (STA) models and microscopic simulators. STA models were the state of practice until recently and they are still utilized substantially throughout the world. Their main deficiency is that they do not model the traffic flow propagation according to the principles of traffic flow theory. As such the practitioners utilize extensive calibration by changing the parameters of the link traffic flow functions to force the OD demand to go through certain paths in order to reproduce the observed traffic counts. In contrast, microscopic traffic flow simulators while they provide the realistic vehicle movements, they do not include the drivers route choices (e.g. no assignment), requiring as an input the travelers path choices. DTA models bridge the gaps of STA models and microscopic traffic simulators through the incorporation of a traffic simulator to move the traffic and a DUE convergent vehicle assignment. A true DTA model should converge to at least a local DUE solution while modeling the flow of traffic based on the traffic flow theory. The principal characteristics of the VISTA-DTA model that was employed for the MISS projected are: The travelers behavior is modeled using a DTA model. It utilizes a universal database model that is based on a spatial Geographic Information System (GIS) that can be easily interfaced with other databases/models. It is Internet and/or Intranet based, providing access to the various stakeholders to run the various algorithms, view the results of the models, query the database, change the database based on the authorization level of each. It has its own dynamic OD estimation algorithm that utilizes as input a static OD matrix and observed traffic counts. The algorithm produces a dynamic OD matrix that reproduces the traffic counts.

6 It utilizes a time-dependent shortest path algorithm Ziliaskopoulos and Mahmassani (1993) that is employed at each iteration of the DTA process. The use of a timedependent shortest path algorithm is essential in DTA modeling. It utilizes an integrated meso/microscopic traffic simulator to propagate traffic at each iteration based on the current network loading. The mesoscopic traffic simulator is based on Daganzos s (1994) cell transmission model. The use of a traffic simulator overcomes the difficulties associated with analytical DTA models by propagating vehicles based on the fundamental relationship between flow, speed and density. It converges to a local DUE solution. The DUE problem is non-convex and therefore any DUE solution can only be claimed to be a local optimum. It can solve any size transport network. MISS-VISTA Incident Impact Analysis Sub-system (IIAS) The main data input requirements by the MISS-VISTA-IIAS are described as follows. Establishment of a calibrated DTA model The transport network infrastructure data, that include the Geographic Information System (GIS), the roadway geometry The traffic control data that include speed limit, vehicle lane designation, movement prohibitions, vehicle classes restrictions (e.g. trucks, autos), signal timing, unsignalized control (Yield, Stop signs, No sign) The traffic flow data that include the static or dynamic OD matrices, link traffic counts, vehicle speed data, travel time data The transit data that include bus/train routes, schedules, transit stations, historical dwelling time at each station. Estimate the impact of incidents on traffic conditions Incident data that include the location of the incident, the roadway capacity reduction or the number of lanes closed, roadway capacity under normal conditions, and estimated incident duration. In addition, the analyst can input a designated diversion route and the percentage of users that are expected to follow their original path or the diversion route. Design methodology The MISS system is designed in such a way that operators of vehicles equipped with the MSCU will send real-time information (accidents, and any incident that causes or may cause any capacity reduction) to the MISS UOC for further analysis and response. In addition, the MISS traffic surveillance system may produce automated notifications of incidents. Under the MISS system, a Video Image Processing (VIP) was developed that can produce estimates of capacity reduction events such as illegal parking, double parking, accident, presence of vehicle(s) on the shoulder of motorways and presence of pedestrians at unauthorized locations. Given the incident data, the MISS-VISTA-IIAS is activated to produce the corresponding impacts on traffic flow conditions. Output The main output data for incident conditions and corresponding normal conditions per time period of day are as follows. Network or Sub-network travel time and delay

7 Selected OD path travel time and delay. In case where one or more diversion routes are designated by the analyst, the software will produce the corresponding statistics based on the percentage of travelers that will use their original route and the new route. This module allows the analysts to evaluate various diversion plans prior to implementation User class (e.g. trucks, autos, other) travel time and delay Bus route travel time and delay including any potential arrival delay at each bus stop Air quality emissions estimates. Prototype development Two prototype MISS-VISTA-IIAS were developed during the MISS project, one for the network covered by the Province of Bologna, Italy and the second one for a portion of the Nicosia, Cyprus transport network. The Province of Bologna and the Cyprus Public Works Department provided the necessary data for the development of the two models, respectively. The paper will present a set of selected scenarios involving various types of incidents. Example of the MISS-VISTA-IIAS The data provided here are based on a sample network, which are provided here to illustrate the main characteristics of the VISTA-DTA, utilizing its incident module. Once the data from PROBO and Nicosia are available an update of the paper will be send. The VISTA software requires as input the location of the incident, the number of lanes closed, the roadway capacity. The total simulation time is 9600 seconds and the assignment interval is 900 seconds - The assignment interval is recommended to be less or equal to 900 seconds in order to capture more detailed traffic flow information. The VISTA RouteSim simulator moves the traffic every 6 seconds. Incident (construction zone) characteristics: The construction zone closure involves 2 out of three lanes for 180 minutes. The affected link has a length of ft. The closure starts at the 1000 ft mark and ends at ends at the 5000 ft mark. Table 1. Network Overview Item Counts in network scenario Counts in network base Nodes Links Signalized Intersection O-D pairs Demand Total Demand Passenger Vehicles Demand Commercial Vehicles Assignment Length (s) Assignment Interval (s)

8 Table 2. Vehicles Overview for network base All Type Vehicles Passenger Veh. Commercial Veh. Loaded Nonentered Nonexited Through Entered Total TT(H) AVG (M) STD (M) ,64 3, ,54 3, ,22 1,87 Entered Veh. VKT (Km) Through Veh. VKT (Km) Table 3. Vehicles Overview for network scenario All Type Vehicles Passenger Veh. Commercial Veh. Loaded Nonentered Nonexited Through Entered Total TT(H) AVG (M) STD (M) Entered Veh. VMT (Miles) Through Veh. VMT (Miles) ,09 11, ,42 10, ,53 17, Table 4. Directly Impacted Vehicle Travel Time Information All types of Vehicles Passenger Veh Commercial Veh Avg Travel Time(M) in scenario 29,1 28,43 31,08 Travel Time STD(M) in scenario 15,96 15,71 16,51 Vehicle Count in scenario Avg Travel Time(M) in base 10,84 10,79 11,01 Travel Time STD(M) in base 1,84 2,06 0,89 Vehicle Count in base Table 5. Directly Impacted Vehicle Delay Information All types of Vehicles Passenger Veh Commercial Veh Avg Delay(M) in scenario 21,5 20,75 23,74 Delay STD(M) in scenario 16,41 16,09 17,15 Vehicle Count in scenario Avg Delay(M) in base 0,34 0,4 0,18 Delay STD(M) in base 0,57 0,64 0,23 Vehicle Count in base

9 Table 6. Delay information (All types of Vehicles) Delay Changes (Minute) -15 ~ ~ ~ ~ ~ Average Change(Min) 4,16 Standard Deviation(Min) 11,52 No. of Vehicles diff (scenario - base) The VISTA model could also produce any statistics at the OD level, path or level and time period of the day. It is recommended that the DTA model is executed for a full 24-hour time-period (simulation period) using a 15-minute assignment time period in order to model all vehicles from their origin to their destination. In addition, it is recommended that a meso-microscopic traffic simulator is employed for sub-networks where the analyst would like to present the traffic flow propagation more accurately. The VISTA software employs such a microscopic traffic simulator that is integrated with the RouteSim mesoscopic traffic simulator. It is envisioned that transport agencies will enhance their existing transport planning and simulator models through the integration of DTA models. It is further envisioned that DTA models will be directly connected to the traffic monitoring systems and executed in real-time, providing more timely traffic flow estimates that could be used for real-time operations and traveler information. Conclusions The MISS platform provides a prototype DTA-based transport and RAA model that could be used by other transport agencies to enhance their view of their respective transport network. The main result is a unified model that is comprised of the following sub-models: 1) MISS- VISTA-DTA - traffic flow conditions at the network/sub-network level, OD level, path and link level;2) MISS-VISTA-IIAS - impact of incidents on traffic flow conditions, and 3) MISS- VISTA-RAA - the incident risk at each link of the network. DTA provides a tool that could be used to estimate the traffic conditions, given the roadway geometry and topology, a dynamic OD matrix for all classes of vehicles, bus and train schedules, and traffic control characteristics. A unified approach was developed where the accident data are normalized using the DTA model, which produced traffic flow rates for roadway links with no traffic counters in place. In addition, in order to develop estimates of the risk at each network link and/or movement, the team devised an innovative approach where the accident records were combined with the opinion of experts from PROBO and the ATC (public transport authority of Bologna). The MISS prototype concept is: Continuously detect and record incidents, roadway hazards and weather information through an automated traffic monitoring system and send them to the UOC. Authorized operators of vehicles equipped with the MSCU unit will observe, record and transmit information to the UOC in real-time. The UOC will utilize the historical and real time traffic flow data to produce estimates of traffic conditions under normal and incident conditions. In parallel, the UOC will produce updated risk GIS maps based on the estimates of the traffic flow conditions, historical incident data and real-time data. The establishment of such a system is expected to improve traffic safety through more proactive actions by the transport authorities,

10 more robust incident management plans and through more comprehensive analyses that will utilize a continuously calibrated DTA-based model. The research conducted for the MISS project is still ongoing and the work is in progress. Despite the data unavailability difficulties and by means of the latest transport modeling algorithms such as DTA, the risk index thematic maps have been produced and integrated in the MISS-UOC. It is believed that the research towards the accomplishment of the project s objectives is going to pay significant contribution both to the E.U. road safety policy with a direct contribution to accident reduction measures as well as to sustainable surface transport in general. References 1. Ben-Akiva, M., H.N. Koutsopoulos and A. Mukudan A dynamic traffic model system for ATMS/ATIS operations. IVHS Journal 2 (1): Daganzo, C.F The cell transmission model : a dynamic representation of highway traffic consistent with the hydrodynamic theory. Transportation Research 28B (4): Mahmassani, H.S., S. Peeta, T.Y. Hu and A.K. Ziliaskopoulos Dynamic traffic assignment with multiple user classes for real-time ATIS/ATMS applications. In Proceedings of the Advanced Traffic Management Conference, St. Petersburg, Florida. Washington D.C.: Federal Highway Administration. 4. Peeta, S. and A.K. Ziliaskopoulos Foundations of dynamic traffic assignment: The past, the present and the future. Networks and Spatial Economics 1 (3/4): Ziliaskopoulos, A.K A linear programming model for the single destination system optimum dynamic traffic assignment problem. Transportation Science 34 (1): Ziliaskopoulos, A.K. and H.S. Mahmassani Time-dependent, shortest path algorithm for real-time intelligent vehicle highway systems applications. Transportation Research Record 1408: Ziliaskopoulos, A.K. and S.T. Waller An internet based geographic information system that integrates data, models and users for transportation applications. Transportation Research 8C (1): af Wahlberg, A. E. Characteristics of low speed accidents with buses in public transport. Accident Analysis & Prevention 34(5): 637, af Wahlberg, A. E. Characteristics of low speed accidents with buses in public transport: part II. Accident Analysis & Prevention 36(1): 63, 2004