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Future Transport Schedule Information Information Technology Essay

In Britain today, ubiquitous technologies are becoming an increasing part of peoples lives. Public transport vehicles often share a road network with other road users making their journeys susceptive to changing road conditions and especially to congestion. Travellers using such public transport increasingly depend on real-time information to plan their journeys. While such information can be provided by Automatic Vehicle Location (AVL) systems, AVLs depend heavily on large-scale deployment of designated sensory equipment, which may prevent their pervasive adoption.

2 Introduction:

Public transport can provide fast, reliable, and 'eco'-friendly transportation at reasonable cost. However, when public transport vehicles, such as buses or tram, are sharing the road or track network with other road or track users, their travel speed and reliability can decrease significantly due to varying traffic conditions and congestion. To ease the impact of varying travel times, public transport providers are inclined to provide up-to-date journey information to travellers using Real-Time Passenger Information (RTPI) systems.

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Such systems may provide information on the current location of vehicles, available routes for vehicle, estimated arrival or travel time of vehicle in stops and should be sent and updated in internet or Mobile networks enabling JIT, passenger waiting times at stops, re-routing or diverting the vehicle due to congestion etc. Travellers can then use this up-to-date information to adjust their travel plans accordingly and ultimately to plan their journey more effectively.

3 Future Transport Schedule Information:

This Coursework presents "21st CTSI" for travellers, an estimation-based approach uses statistical information derived from existing Intelligent Transportation Systems (ITS) in VGF (Verkehrgesellschaft Frankfurt am Main), RMV (Rhein-Main-Verkehrsverbund) and Electronic Maut collect System (implemented by T-Systems ICT GmbH) in Germany.

A prototypical version of the 21st CTSI system has been realized as a part of the 21st CTSIIT ITS framework[1]. Relevant contextual transport information from a prototypical realization of 21st CTSIIT is used to simulate traffic in a real-time, online simulation. From that simulation we can extract future parameters or data for public transport vehicles, which can then be used as part of a RTPI system providing information to travellers.

3.1 Automatic Vehicle location:

Information on vehicle positions is at the heart of RTPI systems and used to calculate travel times and to present useful information to travellers. Data on vehicle location is traditionally gathered using an Automatic Vehicle Location (AVL) system that tracks each individual vehicle and collects the location information in a central depository. This Coursework presents "TSI" for travellers,

There are three major categories of approaches and these approaches are still applied in today's AVL systems. These include:


Proximity based


The Radiolocation-based approach uses triangulation, where a sensor receives radio signals from different transmitting stations and uses the delay of the different signals to calculate its position. Currently, the OBS (on board unit) Global Positioning System (GPS) is the main source of signals for Radiolocation. Satellite-based Radiolocation systems have replaced many of the other AVL systems since the GPS signals can be used free of charge. The number plate automatic recognition system like C-charge in London can be fit only in the city. If bus or tram has to travel one county into other county then this system will need thousands of dual scanner cameras. Which is not cost effective?

3.2 21st CTSIIT Framework:

The "21st CTSI" vehicle system has been designed based on the "21st CTSIIT" ITS framework. As illustrated in Figure 1, the 21st CTSIIT ITS architecture structures legacy systems, 21st CTSIIT systems, and context-aware, end-user applications into three tiers. These tiers define the relationships between systems and applications, and provide a scalable approach for integrating systems, in that individual components can be added to a specific tier without direct consequences to the components in the remaining tiers.


3.2.2 Architecture:

The 21st CTSIIT system architecture supports the integration of legacy systems, and supports future systems that conform to the overall architecture and data-layer.

The purpose of the 21st CTSIIT tier is to integrate transportation systems that model spatial information and implement the Spatial Application Programming Interface (Spatial API) [6]. Therefore, this tier comprises of a federation of transportation systems that implement the spatial data layer. The data layer is distributed across these 21st CTSIIT systems, with each system implementing the subset of the overall layer that is relevant to its operation. 21st CTSIIT systems maintain their individual information, which is often gathered by sensors or provided to actuators, by populating the relevant part of the spatial data layer. However, some of the information maintained in an 21st CTSIIT system specific part of the data layer may actually be provided by underlying legacy systems. Most significantly, traffic information captured in this tier is maintained with its primary-context, and persistently stored data is geo-coded typically by systems exploiting a database with spatial extensions. The 21st CTSI vehicle system exists in the 21st CTSIIT layer and implements its part of the spatial data layer, namely, the vehicle locations. It implements the Spatial API to enable access to vehicle location data and queries information from other relevant 21st CTSIIT Systems. The application tier includes pervasive value added services that provide context-aware user access to traffic information. These services use the distributed data layer and associated context to access information potentially provided by multiple systems. They could include a wide range of interactive (Internet-based) and embedded control services, ranging from the monitoring of live and historical traffic information to the display of waiting times at bus stops or tram stops.

3.2.3 Common Spatial Data Layer:

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The spatial data layer, common to all 21st CTSIIT systems, is comprised of a set of potentially distributed sub-layers and represents the central component of these systems. Individual 21st CTSIIT systems implement one or more of these sub-layers (or parts of sub-layers) and maintain the static, dynamic, live, or historical traffic data available in that sub-layer. For example, a system might implement a sub-layer describing the current weather conditions, while another sub-layer capturing intersection-based traffic volumes might be maintained by a different system. This allows the 21st CTSI system to query all necessary data using a common mechanism, the Spatial API. Furthermore, other 21st CTSIIT systems can use the same common interface to access and retrieve the data generated by 21st CTSI.

3.3 The 21st CTSI System:

The 21st CTSI vehicle system uses the data sharing capabilities of the 21st CTSIIT architecture to access contextual information from a range of legacy ITS systems. The information derived from these systems is then used to estimate the location of public transport vehicles and the resulting data is provided as part of the overall spatial data layer.

OBUs work via GPS and the on-board odometer or tachograph as a back-up to determine how far the lorries have travelled by reference to a digital map and GSM to authorise the payment of the toll via a wireless link.


[1] R. Meier, A. Harrington, and V. Cahill, "A Framework for Integrating Existing and Novel Intelligent Transportation Systems," in Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems (IEEE ITSC´05). Vienna, Austria: IEEE Computer Society, 2005, pp. 650-655.

[2] Riter, S., McCoy, J. "Automatic vehicle location - An overview" in IEEE Transactions on Vehicular Technology. Vol. 26, no. 1, pp. 7-11., February 1977

[3] Zhao, L., Ochieng, W.Y., Quddus, M.A., Noland, R.B., "An Extended Kalman Filter Algorithm for Integrating GPS and Low Cost Dead Reckoning System Data for Vehicle Performance and Emissions Monitoring" in Journal of Navigation (2003), 56, pp. 257-275, Cambridge University Press, Cambridge, UK, May 2003

[4] Shyang-Lih, C., Li-Shien, C., Yun-Chung, C., Sei-Wan, C., "Automatic license plate recognition" , in IEEE Transactions on Intelligent Transportation Systems, Volume: 5, Issue: 1, pp. 42-53, March 2004

[5] Caulfield, B., O'Mahony, M. M., "The provision of on street passenger information via real time passenger information; A case study of Dublin", in Proceedings of the IEEE Conference on Road Transport Information & Control 2004 ( RTIC2004), pp. 1-10, London, UK, 20 - 22 April 2004

[6] R. Meier, A. Harrington, T. Termin, and V. Cahill, "A Spatial Programming Model for Real Global Smart Space Applications," in Proceedings of the 6th IFIP International Conference on Distributed Applications and Interoperable Systems (DAIS 06), LNCS 4025. Bologna, Italy: Springer-Verlag, 2006, pp. 16-31.

[7] Krajzewicz, D., Hertkorn, G., Rössel, C., Wagner, P., "SUMO (Simulation of Urban MObility); An open-source traffic simulation" in ~Al-Akaidi, A. [Ed.]: Proceedings of the 4th Middle East Symposium on Simulation and Modelling (MESM2002), SCS European Publishing House, pp. 183-187, Sharjah / United Arab Emirates, Sept. 2002

[8] Wahle, J., Neubert, L., Esser, J., Schreckenberg, M., "A cellular automaton traffic flow model for online simulation of traffic", in Parallel Computing, Volume 27, Number 5, April 2001 , pp. 719-735

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