ROUTE PLANNING METHODOLOGY OF AN ADVANCED TRAVELLER INFORMATION SYSTEM IN VILNIUS CITY

. As a subsystem of an Intelligent Transportation System (ITS), an Advanced Traveller Information System (ATIS) disseminates real-time traffic information to travellers. To help them with making better decisions on choosing their routes, a strong need to predict traffic congestion and to disseminate the predicted congestion information relating to travellers can be seen. This paper describes a methodology used by drivers for calculating an optimal driven route in Vilnius. The paper discusses how ATIS systems will likely evolve the experience of Information Service Providers (ISP) and optimal route planning calculations. A few methods of route planning have been taken into account. The paper presents the following types of route calculation: 1) the shortest route; 2) the quickest route; 3) the quickest forecasted route according to historical traffic information. Also, the paper deals with the architecture of the WEB based information system for drivers in Vilnius and analyzes data on traffic workflow. Furthermore, a comprehensive route planning procedure that forecasts data on driving time considering historical traffic is followed.


Introduction
e deployment and operational e ciency of Advanced Traveller Information Systems (ATIS) entail the accurate modelling of driver route choice behaviour under real-time tra c information and the calibration of the associated model parameters (Ran 2000). Driver enroute routing decisions are in uenced by personal attributes, response attitude to the supplied information and situational factors such as time-of-day, weather conditions, trip purpose and ambient tra c conditions (Jarašūnienė 2007;Jarašūnienė and Jakubauskas 2007). e latent preferences of drivers towards possible routes are typically di cult to capture accurately because they are signi cantly a ected by past experience, a subjective interpretation of tra c information provided and personal attitudes dynamically changing tra c conditions. e most existing models are limited in their ability to capture the interacting e ects of various situational factors and typically cannot adjust model parameters in a within-day context (Ruiz et al. 2004;Daunoras et al. 2008;Akgüngör 2008aAkgüngör , 2008bMatis 2008Matis , 2010Kinderytė-Poškienė and Sokolovskij 2008;Gowri and Sivanandan 2008;Niewczas et al. 2008;Mesarec and Lep 2009;Çalışkanelli et al. 2009;Junevičius and Bogdevičius 2009). e latter capability is critical for consistency-checking procedures for the real-time operational deployment of advanced information systems. Driver route choice models under information provision have traditionally adopted the econometric theory of random utility maximization (Ben-Akiva and Lerman 1985). Mahmassani and Liu (1999) used a multinomial framework to model and calibrate commuter joint pre trip departure time and route-switching behaviour in response to ATIS based on data obtained from a laboratory interactive dynamic simulator. e study suggests that commuters switch routes if the expected travel time savings exceed indi erence band varying with remaining trip time to destination. Abdel-Aty et al. (1997) developed logic models to capture the e ect of tra c information on commuter route choice using stated preference data. ey analyzed the in uence of travel time variability and the e ect of information on it. e choice between a longer route with reliable travel time and a shorter route with uncertain travel time is investigated based on the notions of risk aversion and risk-taking in route choice. Peeta and Gedela (2001) produced logic models to predict drivers' decisions on route diversion under tra c information provided via variable message signs. ey showed that a strong correlation existed between message content and driver route diversion decisions which could be a control variable in operational strategies to enhance network performance. Khattak and De Palma (1997) use the ordered probity models to investigate the e ect of the weather on traveller behaviour and suggest that commuters change their travel patterns systematically under adverse weather.
In the context of route driver behaviour under information provision, qualitative phenomena such as inertia, compliance, delusion, freezing and perception of tra c information have been recently identi ed. Srinivasan and Mahmassani (2000) made a multinomial model with a nested choice structure to examine inertia and compliance. Inertia represents propensity to remain on the current path while compliance represents the tendency to choose the path recommended by the tra c information system. GIS could be also applied searching for rational car routes in case it is necessary to be at a speci c spot of the network at speci c time. e system o ers a rational route, departure time and calculated expenses depending on the set parameters and real-time tra c data to the user. In other papers analyzing the application of GIS technologies for sustainable transportation, an application of multi-modal networks could be spotted. ey are applied when solving tasks for choosing routes within the overall urban transport system and include cars, public transport, railway transport and even pedestrians (Abdelghany et al. 2001;Geertman and Van Eck 1995). An assessment of transport jams on the street network is necessary when solving the task of choosing the fastest route. A search algorithm the results of which depend on the time set by a system user has been programmed as street capacity is di erent depending on the time of the day (Peeta and Yu 2005;Jakimavičius and Mačerinskienė 2006;Jakimavičius and Burinskienė 2009). General pub-). General public might be the user of IS which helps with choosing a rational driving route. When applying ESRI technologies, a system user is given WEB based GIS application the functioning of which allows setting the start and end of the trip and interim stopping places (if necessary). e IS user is given a map with a marked rational driving route according to real-time tra c data. e system also could forecast a rational driving route taking into consideration historical tra c data.

Transport System Indicators in Vilnius City and a Description of an Information System for Drivers
Growing Lithuanian economy and the increasing quality of living conditions prompt population mobility, motorization level and increasingly high transport ow on streets and roads across the country ( Many scienti c researches analyze a transport information system from the point of intelligent transportation according route planning functionality (Szűcs 2009).
Vilnius information system developed for drivers has route planning functionality. A system user can perform the shortest, the quickest based on real-time tra c information and the quickest forecasted route in accordance with historical tra c information data. e interface of Vilnius information system for drivers is presented in Fig. 1.

Rational Route Planning Algorithms
e road network (as a part of infrastructure) can be represented as a graph. ere are some shortest path algorithms that allow fast point-to-point queries in graphs using pre-processed data. A standard algorithm for this problem is the one developed by Dijkstra algorithm (Dijkstra 1959).
Other scientists adapted Dijkstra's shortest path algorithm to decrease the size of search space (Cherkassy et al. 1996). e algorithm was the rst to use a bi directional search method. is algorithm consists of a forward search from an origin node to the destination node and a reverse search from the destination to origin node. e best rst search has been a framework for heuristics which speeds up algorithms using semantic information about a domain. It has been explored in database context for single pair path computation. A* is a special case of the best rst search algorithm. It uses an estimator function f(u, d) to estimate the cost of the shortest path between nodes u and d. A* has been quite in uential due to its optimality properties (Goldberg and Harrelson 2005).
e Arc-Flags (Möhring et al. 2005;Köhler et al. 2006) method is a modi ed Dijkstra algorithm in order to avoid exploring unnecessary paths. is means the procedure checks the ag entry of the corresponding target region (the region where the target node t belongs to) every time before Dijkstra algorithm wants to traverse an arc which is the only modi cation to the standard Dijsktra algorithm.
Highway Hierarchies (Sanders and Schultes 2006) uses the concept of local search. is approach is a purely hierarchical method, i. e. an approach trying to exploit the hierarchy of a graph. e contraction phase builds the core of a level and adds shortcuts to the graph. e identi cation of highway edges is done by local Dijkstra executions.
In order to perform rational route search in Vilnius information system for drivers, the weighted Dijkstra method have been employed. is method corresponds to further biasing forward search towards the destination. Road network edges have assigned the weighted network attribute parameter (time or length). A route calculation mechanism tries to minimize the total sum of weight associated with a speci c road network edge.

e Methodology Used for Making Route Planning Functional in Vilnius ATIS
Standard ESRI ArcGIS Server Network extension enables to perform optimal route calculation tasks. It is possible to make the quickest route calculations because attribute data on road and street segments are periodically updated by driving time information from the equipped sensors. Fig. 2 presents tra c ow data from tra c sensors equipped on a street to road network GIS data base.
All data on driving time from tra c sensors is generated by one xml le. Each minute, a new xml le is generated. Accordingly, xml le road network database is updated periodically in 5 minutes. e updated road network database contains data on one week travel time in one hour intervals and there are two elds updated periodically in 5 minutes. us, these elds contain almost real-time tra c data. Historical travel time data, which is older than one week, is stored in a separate table. e system takes into account data on real time, driving time and historical driving time obtained in street segments according to the user entered optimal route query parameters. Also, the system allows combining the planned route with an alternative for public transport. WEB application in a new window shows the   Fig. 2. Tra c data work ow optimal route to public transport in a digital map. e types of public transport vehicles and time table schedulers are also presented. Fig. 3 discloses a route planning activity diagram of an advanced traveller information system in Vilnius city.

Mathematical Model for Optimal Route Calculation Tasks
e created GIS transportation data model and developed WEB mapping application allow for public users making the following combinations of optimal driving route calculation: -the shortest optimal route; -the fastest optimal route according to almost real-time driving time data; -optimal driving route according to prognosis alternatives using historical driving time data. e above mentioned optimal route calculation tasks belong to the branch of linear network optimization. e main principal of the shortest and quickest optimal route calculation procedure is that so ware tries to nd the route from the start to the end point by minimizing the weights of road or street network segments. All those weights are stored in the attribute table of GIS road and street network database.
Optimal route modelling tasks have the following features (Kalanta 2003): -a junction with road and street network matches to the peak of the graph and a section of the street segment matches to the edge of the graph; -road network and street database have m peaks (junctions) on Fig. 4 and evaluated weights between network peaks w ij . According to weight information, we can nd optimal routes between start peak i = 1 and the end peak of route i = m; -weight w ij could be applied considering di er-ent values of parameters such as the distance from peak i to peak j, time attribute necessary to make a trip from peak i to peak j. Also, trafc volume from peak i to peak j as well as other interesting tra c conditions could be evaluated; -variables x ij only may have 0 or 1 values. If the shortest, quickest or the route that has the least tra c volume passes peaks i and j, then, x ij = 1 if the route does not go through peaks i and j, then x ij = 0; -N i is the collection j of streets going from peak i; N i + is the collection j of streets going to peak i: When the user of WEB application performs the shortest, the quickest or the optimal route according to historical tra c data, the system computes the optimal route following the below mathematical model: when: (1) x ij 0 t ; i = 1, 2, 3, …, m; j = 2, 3, …, m.
e objective function minimizes the route depending on the values of parameter w ij . Weight is a number value containing such values as the length of the street segment, driving time necessary to go through a particular line segment, the historical driving time of a speci c road or street segment. WEB application computes the shortest driving distance route when weight w ij is lled with street network segment length values. When w ij is lled with road and street network driving time values, WEB application computes the quickest driving route.
Restrictions (1) are used to keep the equation in equilibrium. It describes a condition that only one direc-direction should be used from the rst peak in the optimal route calculation procedure. Equation (2) means that if a certain road and street junction is taken to come to a certain peak, another junction is taken to leave a certain graph peak.

Application and Results of the Method Performing the Quickest Driving Route Calculation
In order to nd the optimal driving route in the road and street network between the start and end points (the shortest and the quickest according to the almost realtime data and the quickest future prognosis according to historical driving time data), it is necessary to evaluate the weights w ij of the network segments. e number values (segment length, driving time) should be attributed to the street sections. e calculation method of the quickest driving route is shown in the below gure. Let's presume that we have the street network fragment shown in Fig. 5. e optimal driving route could be estimated when driving time value for each network segment is known. Besides, it is necessary to know tra c restrictions including one way directions, restricted turns and two level crossings.

Conclusions
1. e created WEB application for optimal driving route calculation can help the WEB user to nd and choose the optimal route depending on optimality criteria chosen by the user: the shortest route, the quickest route according to the present tra c situation and the quickest route that has minimal driven time in agreement with historical tra c data. 2. Dijkstra algorithm has been applied to make calculations nding the optimal driving route. Street sec-Street sec-treet sections have been given weights in the database: street segment length, driving time necessary to pass street edge and historical driving time needed to go through the street segment when performing a route calculation task for the nearest future.
3. e method of driving time estimation is developed calculating the optimal driving route under conditions when the street segment has di erent tra c restrictions such as one-way tra c, restricted turns, prohibited tra c in a particular section. 4. e development of an automatic tra c management system in Vilnius city allowed increasing the average speed of tra c during peak tra c ow by 6%. e considerable growth in tra c speed is registered in the street sections of Vilnius city tra c corridors where tra c speed increased more than 6%, whereas in the street sections crossing tra c corridors, trafc speed decreased about 3%. An automatic tra c light management system arranged in the corridors and determined priority for tra c ow going through  Fig. 6. Calculation results of the quickest route the corridors are the main reasons for the encountered situation. 5. e developed mechanism for a rational choice of the route and designed vector database are published on the general GIS for public use with the help of ESRI technologies. Also, GIS database and a mechanism of updating the road network tra c database could be successfully adapted in other Internet information systems.