APPLICATION OF MULTI-LEVEL TRANSPORT MODEL FOR THE TRISTAR SYSTEM

Transcription

1 APPLICATION OF MULTI-LEVEL TRANSPORT MODEL FOR THE TRISTAR SYSTEM Jacek Oskarbski, Highway Engineering Department, Civil & Environmental Engineering Faculty, Gdansk University of Technology, Poland. (corresponding author) Kazimierz Jamroz, Highway Engineering Department, Civil & Environmental Engineering Faculty, Gdansk University of Technology, Poland. Krystian Birr, Highway Engineering Department, Civil & Environmental Engineering Faculty, Gdansk University of Technology, Poland. Abstract The paper presents the structure of Transport Planning System (TPS) as well as method to supply the Multilevel Model of Transport Systems (MST) with data from TRISTAR system and the characteristics of variables used to power MST. A key element of such a model will be the possibility to acquire and use data from the TRISTAR. Models of transport systems (in the first step for the city of Gdynia) are developed within the EU project CIVITAS DYN@MO. 1. INTRODUCTION Transport telematics technologies offer tools for strengthening transport systems in cities by rationalizing the use of existing infrastructure, increasing its reliability and impact on transport behaviour change of residents, while reducing the operating costs of transport. The main reason for the use of intelligent transport management systems is the need to implement measures to reduce the negative effects of the development of the automotive industry and increase the quality of transport services in variable transport demand. The use of modern technologies can improve the operational area of traffic management, but can also support activities related to the planning and design of transport systems solutions to current traffic organization. Given the above benefits the Tri-City authorities have taken steps to implement the measures of the ITS. Gdynia, Sopot, and Gdansk are three Polish cities located very close to one another at the Baltic Sea coast. They form an agglomeration referred to as Tri-City whose total surface area is 4152 and whose population is 750,000 persons. The Tri-City has nearly 1,400 km of roads, 300 signalized intersections and over 2,000 daytime public transport lines (buses, trams, trolleybuses and local trains). The Tri-City faces transport-related problems that are typical of large agglomerations. In the modal split the share of public transport has decreased in the recent years and the share of cars has increased. Currently the modal split in non-pedestrian travel is 50%/50% and the car owner ship rate is approx. 500 cars/1,000 inhabitants. Each city has its own administration, to include road management administration and, therefore, cooperation in such a complex area as traffic 1

2 management is a true challenge (Jamroz, Oskarbski, Krukowski,2013). Agglomeration of the Tri- City (Gdańsk, Gdynia, Sopot) authorities have decided to work on the implementation of Intelligent Transportation Systems (ITS) in 2002, when scientists from Gdansk University of Technology began conceptual work on agglomeration TRISTAR system. In 2006 the mayors of Gdansk, Gdynia and Sopot signed an agreement to undertake joint activities aimed to prepare a proposal for external funding for the construction of the system, which formed the basis for the start of the cooperation of the various cities in order to achieve the objective on the basis of consensus the detailed concept of TRISTAR system was developed in 2007 (Oskarbski, 2011/1). In the same year, a demo project was implemented in Gdynia along Morska Street, over the distance of 3.6 km which included 9 signalized intersections. As a part of the project, the SCATS system (a traffic control system delivered by Roads and Maritime Services from Australia) and the RAPID system (public transport management system delivered by Sigtec from Australia) were installed; these systems were the initial elements of a priority system for public transport vehicles. The results of the demo project were encouraging because implementation of the SCATS system improved the traffic conditions on the Morska Street despite a small increase in traffic. Improvement was noticed in travel time both by car (a reduction by 12%) and, to a greater extent, by public transport (a reduction by 18.5%). In the 4 years prior to the demo project, the share of public transport travel in non-pedestrian travel decreased by 4.3%. Despite this downward trend, after the system was implemented, the number of passengers in the demo project area increased by 6%. The results enabled defining realistic objectives for the construction of the TRISTAR traffic management project in the Tricity. The objectives are: to improve the traffic conditions by providing traffic management tools and increasing the share of travel by public transport by improving its competitiveness thanks to the use of ITS technologies. After years of work, the project received funding from the Operational Programme Infrastructure and Environment, which allowed to take actions that will end the first stages of implementation of the system TRISTAR in Currently the implementation of the first stages of the system is going on, including the basic layout of the Tricity street with about 150 intersections and pedestrian crossings equipped with traffic signals, communication infrastructure (including fiber optic cable) with a length of about 100 km, installation of 60 video surveillance cameras (CCTV), 60 cameras to identify vehicles (ANPR), about 70 variable message signs at the public transport stops, more than 20 variable message signs for drivers and equipment nearly 700 public transport vehicles in position transmitters and on-board computers (Oskarbski, 2011/2). The target system will be expanded and will include all intersections equipped with traffic signals and will be integrated with the planned systems on express roads (Tricity Bypass, South Bypass) as well as airports, rail, Tricity sea harbours. The project is being implemented in key elements are included in the TRISTAR system architecture. One of the modules of the TRISTAR system is Transport Planning System (TPS) supported by information obtained from other modules of the system. TPS will be integrated with a Multilevel Model of Transport Systems (MST) to allow for increasing the efficiency of the planning, design studies as well as temporary changes to traffic arrangements in connection with road works, special events and changes in traffic in real time, for example, when an accident occurs. 2

3 2. FUNCTIONAL STRUCTURE OF TRISTAR SYSTEM System architecture defines a four-level, hierarchical functional structure (levels of metropolitan, urban, zone and local management). The level of urban management, also called the strategic level or central management layer concerns of urban traffic management in the area of individual cities of Tri-City. Key demands arising from the transport policies of individual cities in Tri-City Agglomeration will be implemented at this level. The primary function of the central level, physically located in the Transport Management Centres in Gdansk and Gdynia, is the integration of all the systems included in the ongoing stage of TRISTAR system implementation (Fig. 1.). Figure 1 Functional structure of first stage of Tristar system implementation(oskarbski, 2011/2). The integration will be achieved through joint hardware and software measures, a common data network and a common database to enable the mutual processing of information provided by the Urban Traffic Management System (UTMS), the Public Transport Management System (PTMS) and Transport Planning System (TPS). The use of hierarchical and modular structure of the system will allow for the future expansion through the addition of new components and replenishment of new features. The main task of the central system is the integration of systems, subsystems and modules through the collection, processing and distribution of data (Oskarbski, 2011/2). Traffic Control System (TCS) is the element (subsystem) of UTMS. TCS will perform adaptive control of traffic signals at 150 intersections in Tri-City. This will ensure the 3

4 continuity of control on the border areas and ability to configure subareas by operators in the Transport Management Centers. Control parameters (cycle length, the split of green and offsets) at intersections will be determined automatically for each of the subareas by the central level. For this purpose, the control system BALANCE (balancing adaptive network control method)(friedrich & Mertz, 1996, Friedrich et al, 1998) is implemented that using genetic algorithms allows to minimize the loss of time, number of stops and length of queues in the designated area. At the same time the local control system EPICS takes control over each single intersection. The model-based traffic adaptive control method EPICS optimizes on base of the picture of the current traffic situation via the status changes of the detectors. (a deterministic model similarly to the TRANSYT model has been developed for EPICS). The relevant quantities entering the target function of the optimization of EPICS is the total delay and the number of stops of the vehicles, which is calculated by summing over all detected traffic streams with a configurable weight each. Control optimization in EPICS is performed in two steps. In the first step, stage sequence is fixed (calculations carried out for the next 5 seconds using a modified algorithm Branch-and-Bound). In the second step the fine tuning takes place, i.e. the starting times of the interstages are optimized with one second precision using the algorithm Hill-Climbing. Horizon optimization is provided in 100 seconds forward (Mertz, 2000). Signals at intersections according to the defined weights may be controlled more locally (EPICS) or in network. BALANCE developed by the German company GEVAS sends traffic signal plans to controllers (at the macro model in the medium-and long-term periods of 5-15 min), the local level can use them as fixed-time programs or combine with actuated signal programs. The objective function for the optimization of BALANCE model can be dynamically created and modified, also using weights that allow to create it with a combination of multiple criteria of optimization. For signal programs objective function is calculated using the performance index PI, which uses the measured or calculated parameters and their combinations for the signal groups, such as delays, queue length and the number of stops. Component of delivered by GEVAS company software - DRIVERS enables the detection of conditions and disturbances in traffic (it will be possible to extract the data from vehicles in the future) and immediate demand to change signal settings in response to a change in state of traffic. TCS will control traffic with priorities for public transport vehicles. For this purpose appropriate detection equipment in public transport vehicles and solutions for vehicle location provided in PTVMS will be used. Such a solution will enable the opportunity to address the priorities for public transport vehicles on the basis of vehicle delays in relation to the timetable. The primary function of the MTSS will be delivering and collecting in the data warehouse specific information regarding the number and type of vehicles moving in the area covered by the system. This module will primarily use traffic measurement stations installed at intersections and other detectors. MTSS will provide data on the level of service and average speed of vehicles. The purpose of the Video Surveillance System (VSS) will be supplying images from cameras (CCTV) placed at critical points of the road network. The management level will provide access to the requested video cameras via a graphical interface, you can select a camera on the map of the road network. 4

5 The main task of WPMS will be collecting and gathering in databases and data warehouses as well as providing information on meteorological conditions in the Tri- City Agglomeration. PGS will provide information about the availability of parking places in particular areas of cities (parking lots). System on the basis of information supplied by the device counting vehicles in parking lots will determine the number of places available and provide sufficient information to Variable Message Signs. The system must provide current data on the availability of parking in order to allow their use by other subsystems (in particular by PIAS via the website). TSMS will enable the implementation of features such as automatic supervision of the behaviour of road users (i.e. over speeding and passing a red light), the smooth and early detection of dangerous situations and road incidents, rapid reporting to emergency and road services of hazardous incidents and road accidents. The main function of the Driver Information System (DIS) will be data processing, collected in databases and data warehouse of TRISTAR system and transfer them in the form of textual and graphical to road users through Variable Message Signs. The system will manage the sharing of data provided by other systems or entered by the operator. The system will constitute the primary source of knowledge about the situation of the road in terms of the traffic volume, current and predicted traffic conditions, incidents and the planned impediments, information about weather conditions on the roads and warnings of hazardous weather conditions for traffic. Information obtained from DIS will be used by PIAS ( presented on the Internet portal and transferred in the form of radio communications in the future as well as through other media - such as satellite navigation. Passenger Information System (PIS) will be one of the subsystems in the Public Transport Management System (PTMS). PIS will perform the function of informing passengers about the actual departure time of vehicles and the conditions of travel by public transport in the metropolitan via variable message passenger information boards at bus stops as well as via passenger information terminals available at the transfer nodes and in shopping centres. Passenger information terminals and web portal will enable to plan a trip taking into account the delays of public transport vehicles. The main task of the Public Transport Vehicles Management System (PTVMS) will be to keep regularity and punctuality and enable appropriate responses to public transport vehicles to disruption in street network or public transport network. Public Transport Vehicle Location Module will provide information on the geographical location of all public transport vehicles currently in motion on the basis of the information provided with on-board computers equipped with GPS satellite navigation system. Personal Information Access System (PIAS) will enable to get information about traffic conditions before and during travelling by drivers and passengers of public transport vehicles in the Tri-City Agglomeration. As part of the system Internet portal will be initiated, common to all the cities, where the information will be displayed. Internet service will allow to schedule the shortest and/or quickest route, based on public transport timetables of public transport (including local train) and information about the delays from PTVMS and other subsystems. 5

6 Transport Planning System (TPS) will be assisted with tools (software packages) useful in the planning of transport systems, analysis of traffic conditions and the testing and simulation of solutions to the traffic organization planned to introduce. As part of the TPS a multi-level model of transport systems (MST) is being implemented, which will enable the development of analysis and traffic forecasts for planning purposes, but also for the needs of the current traffic management. MST will use data necessary for calibration or simulation directly from the databases and data warehouse of TRISTAR system (Oskarbski, 2011/2). 3. MULTI-LEVEL TRANSPORT SYSTEMS MODEL Planning of transport networks is a fairly complicated process, because for its construction and expansion weighing decisions are needed. Building of efficient transport networks requires to solve complex optimization problems in transportation. The results of analysis carried out by using various tools, which include various types of forecasting software are used to facilitate the process of making planning decisions. Forecasting is based on scientific analysis, to formulate opinions about the future state of the studied phenomenon on the basis of knowledge about its current course. In the case of transportation systems forecasting mostly concerns the prediction estimation of the future (medium or long term) traffic volume or volume of passenger travel or freight for existing or planned transport network. Need for use of traffic models to forecast and test transport networks is obvious due to the inability to survey the events and relationships occurring in the transport networks in an empirical way (Oskarbski, 2011/2). 3.1 ASSUMPTIONS FOR THE IMPLEMENTATION OF THE MST Experience of conducting analysis and forecasting of traffic in the Pomeranian Region, indicate the need to organize the approach to develop predictive models for the analysis of transport. In taking up this challenge, the given below assumptions for system design concept of forecasting and traffic analysis should be foreseen. According to these assumptions the system of forecasting and traffic analysis should (Oskarbski, 2011/2, Oskarbski & Jamroz, 2014): cover an area of Pomeranian Region (first step is to complete the model for the city of Gdynia within CIVITAS DYN@MO project), or to be fed with data from regional and national model have a hierarchical structure consisting of layers of different management levels enable the efficient exchange of information and data between layers of management enable the provision of data to the various tasks arising from the process of functioning of the transport systems and facilities, which will facilitate optimal decision-making 6

7 provide data for planning work, feasibility studies of transport facilities, projects of changes in the traffic arrangement plans, taking into account geometric solutions at intersections and interchanges and advanced traffic control set up data and information for updating Gdynia s (in the first step) Sustainable Urban Mobility Plan (SUMP) and provide detailed analyses and verification of effects on mobility management initiated by SUMP proposals and measures provide simulation tools to persuade inhabitants to proposed solutions for improving transport system of the city Assumptions given above cannot be achieved with a single traffic or transport model. This will be possible, however, by building an integrated, multi-level model of traffic forecasting and analysis (Table 1). The approach will be multi-level and multi-layer model based on the idea adopted by the Department of Planning and Transportation of the City of London (Smith, Blewitt at al, 2010). The levels are determined by type of administrative area and the layers are determined by kind of transport analysis or transport management. MST (Multilevel Model of Transport Systems) will be fed with data from detection systems located in the architecture and infrastructure of the TRISTAR system and ultimately also using localization devices in vehicles, mobile phones and navigation devices (Bluetooth, wifi) (Oskarbski & Jamroz, 2014; Jamroz, Oskarbski, Birr, 2013). Strategic level of MST includes the provision of data to develop a transport policy, the implementation of planning studies and network studies. At the strategic level macroscopic model (based on the VISUM program) (PTV AG) will be applied. The model requires disaggregation of districts within the area of the City of Gdynia together with detailed road network and public transport lines parameters input. The model will be used to collect and estimate data for the mezoscopic model, complying with the planned routes located in the vicinity of the city (Oskarbski, 2011/3). Tactical level includes the provision of data to develop a decision-making papers (network and corridor studies, feasibility studies), development projects of traffic arrangement, traffic control and evaluation of planning solutions effectiveness as well as for traffic management purpose. The object of research will be the transport network, a sequence of roads or streets sections, the segment of public transport line in this case. At the tactical level mezoscopic model (based on the VISUM and SATURN programs (ATKINS) as well as BALANCE offline program (GEVAS) implemented within TRISTAR system) will be applied. This model will be used to analyse the scenarios of traffic arrangement modifications as well as to estimate the efficiency measures of planned modifications. The model will derive the results from the macroscopic model within the scope of demand modelling with a parallel division into particular modes of transport. This will enable calibration with taking into account the road network within the Pomeranian Region (results from the macroscopic model). The model will be powered and calibrated with data from TRISTAR (in the future the implementation of the public transport passenger counters in vehicles to update and calibrate the models will also use the data to statistical analysis, respectively). The macroscopic and the mezoscopic models will provide the basis for the elaboration of the microscopic model (simulations) which will be powered by current or historical data from the data warehouse of 7

8 TRISTAR system and traffic control system BALANCE (Oskarbski & Jamroz, 2014; Jamroz, Oskarbski, Birr, 2013). Operational level includes the provision of data to develop specific projects of traffic arrangement, traffic control programs, projects of transportation services for the selected objects and primarily visualize the operation of transport facilities. The object of research will be a section of road or street, section of the public transport line or junction. At the operational level microscopic model (based on the VISSIM program (PTV AG) or DRACULA program (Liu, Van Vliet, Watling, 1995/1; Liu, Van Vliet, Watling, 1995/2) will be applied. This model will allow for verification and demonstration of results obtained from the macro and mezoscopic models and will provide important input for the community projects and the planned internet platform (Mobility 2.0) within CIVITAS DYN@MO to communicate with inhabitants. DRACULA software allows to simulate traffic in an area much larger than VISSIM (the ability to simulate traffic at only a few intersections at the same time). It is very useful for testing control strategies in large areas. As part of the first stages of the TRISTAR system implementation the macroscopic model will be used - for the development of traffic management scenarios in the case of incidents occurrence, road works, public events as well as for planning of new public transport routes (lines) or change routes (lines) of public transport vehicles with the possibility to analyze changes in the timetable (the VISUM). Microscopic model will be used for the simulation of changes of traffic control parameters and analyzes of traffic control strategies (VISUM, BALANCE offline, VISSIM). After implementation of TRISTAR system the models will be supplied with the information on traffic parameters in realtime (Oskarbski, 2011/3). Table 1 - Use of transport modelling tools depending on the level of management and area (Oskarbski& Jamroz, 2014; Jamroz, Oskarbski, Birr, 2013) Area Level of management Object Strategic Tactical Operational Transport network Transport network/street and PT lines Street, PT line, set of junctions, junction Model kind Dedicated - prognostic Advanced Simulation Model type Macroscopic Mezoscopic Microscopic Country VISUM VISUM Region VISUM VISUM Agglome ration Tool VISUM VISUM, SATURN DRACULA City/cord SATURN, TRANSYT, VISSIM, VISUM on/ local DRACULA DRACULA 8

9 3.2 RELATIONSHIP BETWEEN MULTILEVEL TRANSPORT SYSTEMS MODEL AND TRANSPORT PLANNING SYSTEM In the area of Gdynia models will be extended to the entire city and developed within the EU's CIVITAS project (CIVITAS II Plus). The delivered software enables simulation of the effects of road incidents (collisions, accidents, congestion) and special events into transport operations quality. Described models will derive the necessary data for calibration or simulation directly from the databases of TRISTAR system. For the preliminary analysis, aimed at optimizing the parameters of traffic control can be used TRANSYT (TRL) or SATURN programs. TRANSYT program can only be used for the primary analyzes, due to the static - fixed distribution of traffic in the network model, which does not reflect actual travel behaviour of drivers, due to the fact that any change of signalling parameters will change delays, capacity and queue lengths on individual intersections, which in turn changes the choice of routes. It is therefore appropriate to extend the analysis using packages which allow to take into account the dynamic distribution of traffic (e.g. SATURN software). The Transport Planning System also involves the use of CROSSIG program (GEVAS) (as a part of VTassist module) to assist in the preparation of traffic signal projects that data on traffic streams will be derived from the BALANCE traffic control system and other elements of a multilevel model. Implementation of traffic control system consists in moving into the controller operating system environment the so-called TRENDS Kernel. The TRENDS Kernel enables embedding in the controller a local Entire Priority Intersection Control System (EPICS). The EPICS system collects traffic data from inductive loops (or other detection systems) at intersections. On the other hand, the Balancing Adaptive Network Control Method (BALANCE) system ensures optimum operation of controllers in groups (control areas). The BALANCE system uses a traffic model, e.g. from the VISUM software, or the integrated DRIVERS and optimizing algorithms as well as VTnet network model. It is possible to change the target functions in the optimization performed by the BALANCE system to reach the best Performance Index. The following factors can be considered: the vehicle delays/the number of vehicle stops/the queue length. The factors can be assigned different weights. The BALANCE system communicates with the traffic signal controllers through a control module (Jamroz, Oskarbski, Krukowski, 2013; Oskarbski & Jamroz, 2014). Data from detection systems of Urban Traffic Management System (component of TRISTAR), such as volumes and speeds, number of stops, queue length and the control parameters will be obtained through a traffic control system BALANCE / DRIVERS. Diagram of transport analysis process using software MST and TPS is shown in Figure 2. In addition, information will be used from traffic measurement stations which will allow detection of the instantaneous speed, traffic density and the traffic structure (class vehicles according to EUR 6 on sections between intersections or according to EUR 3 within junctions) which will allow e.g. trip matrices estimation and calibration for different types of vehicles in a multi-level model. 9

10 Figure 2 Diagram of transport analysis process using software MST and TPS. The use of ANPR cameras in addition to the function of determining the travel time between cameras will also allow for the development of applications, enabling the estimation of trip matrix in real-time for use by SATURN, VISUM or BALANCE offline. Calibrated in the SATURN or VISUM matrices allow analysis of the impact of changes of control parameters on the efficiency of the transport system and selection of traffic control strategies. The SATURN program takes into account the reduction of transport demand and the accessibility of specific areas by using elastic assignment methods. Transport Planning System will be equipped with additional tools provided by GEVAS, enabling monitoring and analysis of quality of implemented priorities for public transport vehicles (VTmonitor / FAS) and the quality of the traffic control system operations (VTnet / pcoq), which will provide additional information on the performance of transport systems operation on individual intersections within the TRISTAR implementation area, in addition to the analyzes carried out by means of analytical models of MST (Oskarbski & Jamroz, 2014; Jamroz, Oskarbski, Birr, 2013). Transport Planning System diagram is shown in Figure 3. Table 2 summarizes the main characteristics of the models in different areas. When analyzing these characteristics can be concluded that the smaller the area of analysis, the greater the assumed accuracy of the model. 10

11 Table 2 - Use of modelling tools and data acquisition (Oskarbski & Jamroz, 2014; Jamroz, Oskarbski, Birr, 2013) Transmission Model Transpor Traffic data - Data on public of data developm t network private transport transport between ent models Model Nationa Area l County Regional Agglomeration Municipality Municipality Municipality / District/Housing estate Transportation analysis zone National, regional National, regional, county roads National, regional, county and important local roads National, regional, county and local roads General measurement of traffic(cross section) General measurement of traffic(cross section), complementary measurements on county roads Information on the number of passengers e.g. on the basis of ticket sales Measurement of the number of passengers in public transport vehicles General Measurement of the measurement of number of passengers traffic (road cross in public transport section), vehicles complementary Speeds obtained measurements on from on-board county and computers in the municipality roads public transport vehicle speed vehicle location system measurement. Automatic detection Speeds and traffic of the number of volumes derived passengers within from traffic control TRISTAR. system. 11 Information on through and OD trips on the cordon of the area from the national model Information on through and OD trips on the cordon of the area from the regional model General Measurement of the measurement of number of passengers Information on traffic (cross section in public transport through and OD of road and turns at vehicles, public trips on the intersections), transport vehicles cordon of the vehicle speed speeds measurement area from the measurement. Automatic detection regional or Speeds and traffic of the number of agglomeration volumes derived passengers within model from traffic control TRISTAR. system. Simplified procedure Simplified procedure Developed procedure, study of inhabitants transport behaviour on a small sample data gathered from the TRISTAR system allow its extension Developed procedure, comprehen sive transport study data gathered from the TRISTAR system allow to calibrate and verify model

12 Figure 3 Transport Planning System diagram process cycle. 3.3 DATA FROM THE TRISTAR SYSTEM USED TO POWER THE MTS Sample of the data source that will be used to power the MTS and will form the basis and sources for carrying out research are presented below. Raw data will be stored in the original database, which will be divided into two areas Analytical Database (data source registered as a single event) and data warehouse (aggregated data e.g. to power MST). The first area (Analytical Database) will contain the source data recorded as a single occurrence related to: 1. Detectors (data from TCS and MTSS) detector identifier (unique in the Tri-City) incident time (beginning occupancy of edge) with an accuracy of sec. occupation time of the detector with an accuracy of sec. 2. Request for public transport vehicles priority points (data from TCS - module of priorities for public transport) intersection identifier (unique in the Tri-City) trajectory ID / signal group (unique in the Tri-City) PT (public transport)line identifier (unique in the system) ID route (unique in the line) desired priority level (0-3) depends on PT vehicle delay in relation to the timetable 12

13 check in time at initial point (accuracy of 1 sec.) check in time at main point (accuracy of 1 sec.) check out time at the last point (accuracy of 1 sec.) 3. Signal groups (change of state of signal groups) data from TCS signal group ID (unique in the Tri-City) occurrence time (the beginning of the signal) accuracy to 1 sec. duration of the signal (an accuracy of 1 sec.) signal code 4. Signal plans (signal plan change) data from TCS controller ID (unique in the Tri-City) event time (the beginning of the plan) an accuracy of 1 sec. plan number (including transitional plans) 5. Traffic measurement stations (from MTSS) measurement station identifier (unique in the Tri-City) occurrence time with an accuracy of sec. vehicle speed (an accuracy of 1 km/h) length of the vehicle (with an accuracy of up to 0.1 m) occupation time of measurement point by vehicle (accuracy of 0.1 sec.) the time interval from the previous vehicle (to the nearest 0.1 sec.) vehicle class (0-4) 6. Weather Stations measuring meteorological parameters (parameters recorded every 10 minutes) data from WPMS station ID (unique in the Tri-City) data averaging end time (an accuracy of 1 sec.) air temperature air relative humidity pavement temperature pavement base course temperature pavement condition thickness of the ice layer concentration of the chemical agent intensity of precipitation type of precipitation power of the wind wind direction 7. ANPR (Automatic Number Plate Recognition) cameras (license plate recognition) from MTSS measurement point identifier (unique in the Tri-City) time of the occurrence (accuracy of 0.1 sec.) 13

14 hash (the first three letters of the registration number) segment between two measurement points identifier (unique in the Tri-City) registered travel time between points (accuracy of 0.1 sec.) 8. ANPR cameras (passing red light detection and over speeding at measurement point or on the road section) - from TSMS measurement point identifier (unique in the Tri-City) occurrence time (accurate to 1 sec.) recorded speed (an accuracy of 1 km/h) over speed value (with an accuracy of 1 km/h) measuring section identifier (unique in the Tri-City) occurrence time at the end of the section (with an accuracy of 1 sec.) section registered speed (with an accuracy of 1 km/h over speeding on section (with an accuracy of 1 km/h) The second area (Data Warehouse) will contain aggregated data relating to the sections between and within junctions. Data will be aggregated with intervals: 15 min, 30 min, 60 min, 24 hours, a week, month, year. Aggregated data can be supplemented with data calculated with use of models applied in TPS (e.g. traffic on turns at the intersection computed by model DRIVERS, the average speed on the section between junctions, the density of traffic on the section, level of service). In addition, the aggregated data will contribute to the procedures for calculating capacity. Aggregation of data will allow to calibrate the basic parameters such as gaps and headways at junctions (e.g. used in the car following model in VISSIM software and simulation models in SATURN software). In addition, there is the opportunity to provide: development and verification of street section resistance functions in the macroscopic model and mezoskopowym, verification of traffic parameters such as saturation flows, verification of passenger car unit, speed-flow curves on street sections, models calibration using traffic volumes on sections and on turns at intersections, verification of methods for traffic assignment in networks. 3.4 METHODOLOGY OF DEVELOP MST Develop and implement MST for the city of Gdynia MST as a tool for carrying out research within pilot projects and supporting the operational traffic/public transport management, planning and analysis (mainly developing SUMP) and estimating and presenting the results of planning and operational management of transport. Development of MST, fed from the data warehouse of transport management system TRISTAR. The research methodology includes (Oskarbski, 2013): setting up of research stations for testing and building models preparation of databases ( available to collect demographic data, economic, sociological, data related to spatial planning and research traffic and travel via public 14

15 transport, processing data from travel behavior surveys, collect data on the supply layer in modeling : the existing parameters of the network sections, parameters of the intersections, PT lines and stops), analysis of the possibilities of using traffic management system TRISTAR to power MST, development method for power models with data from TRISTAR, development of principles of integration of macroscopic, mesoscopic and microscopic models within zones, nodes and network sections (supply layer) and demand layer (OD matrices), carry out additional traffic measurements ( field research ), carry out tests to calibrate the model - field studies and simulation develop macroscopic, mezoscopic and microscopic models collect data for the control groups to verify the models ( field research ) carry out the calibration and verification of models final evaluation of task Expected results: development of tools supporting the traffic/public transport operational management, planning and analysis in the short and long term time horizons (mainly developing SUMP) and estimating and presenting the results of planning and operational management of transport including cooperation with the system TRISTAR (Oskarbski, 2013). 4. CONCLUSIONS Modern transport systems management requires the use of better tools for forecasting and analysis of transport. Used computer programs enable the development of the transport system model for the selected object or area of street network contain the basic procedures of the mathematical model of the transport system. The role of the analyst is the selection of appropriate transport parameters of these models. Selected parameters should reflect actual travel behaviour of inhabitants and the characteristics of the area and transport networks in the current state as well as the rules for their development. Negative experiences with conducting analysis and forecasting of traffic in the Pomeranian Region require arrangement through the development of an integrated system for forecasting and analysis of transport. This should include specific areas of administrative management and the levels of traffic management. The basic idea is the possibility of cooperation between different systems and the ability to use their results by different institutions for different purposes of transport management. Implementation of new technologies in transport provides access to data, which can power transport models. It will contribute to a reduction in expenses and time which are incurred for transport research and data collecting as well as contributes to increase the accuracy of modelling and improving the quality of transport models. 15

16 BIBLIOGRAPHY Friedrich, B., & Mertz, J. (1996). Abschlußbericht Munich COMFORT Arbeitsbereich 444, Städtische Verkehrssteuerung. München: Fachgebiet Verkehrstechnik und Verkehrsplanung, TU-München. Friedrich, B., Mertz, J., Ernhofer, O., Clark, M., Toomey, C., McLean, T., et al. (1998). TABASCO Deliverable 9.4: Urban Traffic Control with PT Priority: Final Evaluation Report. Brüssel. Jamroz K., Oskarbski J., Birr K. (2013). Multilevel transport system models for traffic management. X Scientific-Technical Conference "Transport Systems - Theory and Practice", Katowice, Poland. Jamroz, K., Oskarbski, J., Krukowski P. (2013). In: The role of the Gdansk Science and Technology Park (Poland) in the creation of technologies that influence the development of cities. In Proceedings 30th IASP World Conference on Science and Technology Parks 2013, Brazil. IASP. Liu R., Van Vliet D., Watling D. (1995/1): DRACULA: Dynamic Route Assignment Combining User Learning and Micro-simulation. Paper presented at PTRC vol. E Liu R., Van Vliet D., Watling D. (1995/2): DRACULA - Microscopic Day-to-Day Dynamic Modelling of Traffic Assignment and Simulation. Paper presented at the Fourth International Conference on Applications of Advanced Technologies in Transportation Engineering. Capri, Italy Mertz, J. [2000]: Ein mikroskopisches Verfahren zur verkehrsadaptiven Knotenpunktsteuerung mit Vorrang des öffentlichen Verkehrs (dissertation). Fachgebiet Verkehrstechnik und Verkehrsplanung Univ. Prof. Dr./UCB Hartmut Keller Technische Universität München Oskarbski, J. (2011/1). Intelligent transport systems for agglomerations on the example of the Tricity, Komunikacja Publiczna 1/2011. Oskarbski, J. (2011/2). Functional structure of the transport management system in the Tricity TRISTAR, Przegląd Komunikacyjny 7-8/2011 Oskarbski J. (2011/3). Perspectives of Telematics Implementation in Tricity Transport Systems Management and Planning. Communications in Computer and Information Science. (Modern Transport Telematics), 239/2011. Springer Berlin Heidelberg. Oskarbski J. (2013) Conception of Multilevel Model of Transport Systems. Internal Report within CIVITAS DYN@MO project. Oskarbski J., Jamroz K. (2014). Multi-level transport systems model for traffic management activities. Paper submitted to 10th ITS European Congress, Helsinki, Finland. Smith, J., Blewitt, R. at al. (2010). Traffic Modelling Guidelines. Traffic Manager and Network Performance Best Practice. Version 3.0. Transport for London. ATKINS, GEVAS, gevas.eu PTV AG, TRL, 16