JPL's Wireless Communication Reference Website

Chapter: Wireless Data Networks
Chapter: Computer Software
Chapter: Network Concepts and Standards; Section: Intelligent Transportation System; Probe Vehicles

PROMOT Computer Program

PROMOT (PRObe vehicle concept for MOnitoring road Traffic) studies the efficiency of the probe vehicle concept in general and the compares the performance protocols for uplink probe vehicle transmission, viz. ALOHA and polling. To a limited extent, it can be used a network planning tool.

Promot runs under DOS, and has a menu structure to enter parameter of the calculations or simulation. It is located at software/ivhsprog.zip. Brief instructions for installation are available.

Disclaimer: The software has no guarantee of any type whatsoever. The user agrees that neither the publisher nor the editor / authors will be held liable for any damages, including lost profits, savings, or any incidental or consequential damages arising out of the use or presence of any program.

The PROMOT Model

A model covering road traffic aspects as well as data communication aspects, has been developed in order to implement a performance analysis of obtaining real-time road traffic data from probe vehicles through a mobile communication infrastructure. This multi-disciplinary model has been called PROMOT (PRObe vehicle concept for MOnitoring road Traffic) and consists of several sub-models.

Network Specification

An infrastructure network incorporates at least two elements, namely links and connecting points. The purpose of links is to concentrate flows of traffic so that a collective infrastructure can be constructed. The connecting points are represented by nodes which connect (dis)similar links and where a network can be entered or left.

At the highest functional level of abstraction of road traffic infrastructure we find the inter- urban roads, such as interstates, express ways and freeways, with the principal objective to connect different areas of community. With respect to their shape inter-urban roads can be classified into five distinct base types, namely: linear or axial, star shaped or radial, circular, rectangular or grid and triangular. With respect to their location in relation to urban agglomerations tangential and radial inter-urban roads can be distinguished. The inter-urban road network in the Netherlands is characterized by a tangential location and a triangular shape. The inter-urban road network in the USA has a grid structure and also a tangential location in relation to urban agglomerations. At a lower functional level of abstraction of road traffic infrastructure we find the urban roads, such as highways, motorways and highstreets, with the principal objective to facilitate the different parts of the urban area. In essence three distinct base types of urban roads can be distinguished, namely: linear or axial, star shaped or radial, and rectangular or grid structure. In particular because urban areas in the Netherlands have generally originated and urban areas in the USA have usually been constructed, Dutch urban road networks are mostly characterized by a circular or radial shape (organic structure), while American urban road networks are mostly characterized by a grid structure.

It is generally recognized that the effectiveness of mobile data protocols depends among other things the number of mobile transmitters, the temporal distribution of mobile transmitters, and the spatial distribution of mobile transmitters. Hence, it can be envisaged that in urban area road networks, with relatively low traffic flows and speeds, and in inter-urban area road networks, with relatively high traffic flows and speeds, the behaviour of the probe vehicle concept will differ. For these reasons, two different types of road networks have been selected in the Network Specification Model (sub-models 1 and 2) to analyze the performance of the probe vehicle concept and to use within the PROMOT model.

The first one is an inter- urban area road network with a grid structure and a tangential orientation, namely the freeway network in the San Francisco Bay Area.

The second one is an urban area road network with a ring-shaped structure and a radial orientation, namely the motorway and highstreet network of Eindhoven.

Each link in the network specification (both inter-urban and urban) contains the following attributes:

  1. - begin node (A-node),
  2. - end node (B-node),
  3. - capacity,
  4. - speed in unloaded condition, and
  5. - one-way or two-way road.

Inter-Urban Road Network For the inter-urban network specification, we took the San Francisco Bay Area (San Francisco- Oakland-San Jose). This area was divided into network nodes representing zones with the most important attributes `dwelling' and `employment'. These nodes are connected by links, representing the road infrastructure. Each trip starts and ends in a certain node.

This Figure shows the abstracted road network of the San Francisco Bay Area. This abstracted network has been limited to the major roads (mostly highways) and comprises 40 zones and 63 links. In order to be able to apply the Transportation Model also the major public transport lines in the San Francisco Bay Area have been added (17 nodes and 17 zones). For trips by public transport the speed outside the public transport network has been taken high (15 km/h), assuming that the car will be used in pre- and post- transport.

Urban Road Network

Monitoring a complete urban area road network by means of infrastructure based traffic detectors would require an immense amount of such traffic detectors due to the small-scale lay- out of such a network, with many short road links and many crossroads, and is therefore hardly possible. A monitoring system with roving probe vehicles would seem more appropriate under these conditions. In order to analyze the performance of the probe vehicle concept in an urban area also an urban area traffic flow computation has been performed. The urban area performance analysis should not only express exterior contrasts, such as differences in shape and traffic load of inter-urban and urban road networks, but also interior differences. The urban area network specification should therefore also consider several types of roads, ranging from small, lightly loaded to large, heavily loaded roads. In particular, the combination of several distinct types of roads is important to investigate whether or not the smaller roads are predominated by the larger roads. For this reason the highways around the city of Eindhoven, the entrance and exit roads to and from the city of Eindhoven, the ring road in Eindhoven as well as the lower class roads have been selected for the urban network specification.

The network specification of the urban area of Eindhoven is based on data from the Dutch Rijkswaterstaat and the municipality of Eindhoven. This data has been aggregated into a road network of 81 nodes and 266 links. This data was sufficiently comprehensive to perform a traffic flow computation for the urban area of Eindhoven and therefore a public transport network specification was not required.

Transportation Model

Most existent transportation models can be brought back to the general structure of the classic four-stage transportation model. Although the classic model is presented as a sequence of four sub-models, it is generally recognised that the process of traveling does not actually take place in this type of sequence. Socio-economic data is "used to estimate a model of the total number of trips generated and attracted by each zone of the study area (trip generation). The next step is the allocation of these trips to particular destinations, in other words their distribution over space, thus producing a trip matrix. The following stage normally involves modeling the choice of mode and this results in modal split, i.e. the allocation of trips in the matrix to different modes. Finally, the last stage in the classic transportation model requires assignment of the trips by each mode to their corresponding network: typically private and public transport.".

For the urban traffic flow computation all required travel and traffic data was available to perform each of the above-mentioned steps.

For the inter-urban traffic flow computation straight-forward execution of the above- mentioned steps was not possible as accurate data about trip generation was not available. For this reason a divergent Transportation Model, which integrates the first three steps of the classic transportation model, has been used to compute this trip generation data from available, but much more global, data.

Inter-Urban Road Network

The theoretical foundation for the Transportation Model is a micro-economic theory under money and time constraints, stating that each persons maximizes the difference between utility of where he/she lives and works and the sacrifice (generalized time, which expresses an average of different sacrifices needed for traveling, such as travel time and costs, expressed in units of time) of the commute.

The Transportation Model computes the trip generation, that is the number of departures and arrivals (trip ends) of each network zone, assuming accessibility to be the main factor influencing the process of trip generation. Therefore, the components distribution and modal split are calculated simultaneously and elastic, rather than fixed constraints are used. In this way, the land-use or trip generation data (defined as employment and working population) that are traditionally exogenous in a fixed constrained interaction model, are computed endogenously.

Urban Road Network

For the urban traffic flow computation measured data was available (from the MER/trace study into construction of the motorway A50. Because the area subdivision in both studies slightly differed, the available data had to be adapted to fit the specified urban road network of Eindhoven. Subsequently, the same distribution computations were performed as described in the previous sub-section (the available data concerned road traffic only and therefore the modal split could be omitted).

Inter-Urban Road Network

Figure 5.6 illustrates the road traffic flows during the evening rush hour on the inter-urban freeway network of the San Francisco Bay Area, found according to the assignment scheme discussed above. The thickness of the links indicates the size of the traffic flow qa. The different hatches of the links represent qa / ca, i.e., the amount of vehicle traffic traveling on the road link relative to the capacity of that link and provide estimates of link travel times. A ratio of qa / ca is 0.85 or higher indicates congested traffic.


PROMOT Screen Dump: Intensity of messages transmitted by probe vehicles in San Francisco Bay Area. Penetration 1%, one transmission per probe vehicle every 60 sec.

Figure 5.6 Computed road traffic flows during the evening rush hour on the inter-urban freeway network of the San Francisco Bay Area Detailed validation of the estimated road link flows with real traffic measurements could not be done as no accurate data was available. Rough comparison with the traffic situation on the freeways in the San Francisco Bay Area during average peak hours indicated that the estimations are realistic. For our purpose, i.e., creating a sufficiently realistic traffic situation to evaluate the performance of the probe vehicle concept for collecting traffic messages in an inter- urban road network, this verification is believed to be adequate, as it pictures an average traffic situation in the Bay Area.

Urban Road Network

The road traffic flows are computed for the evening rush hour on the urban road network of the city of Eindhoven, and are found according to the assignment scheme discussed above. Again, the thickness of the links indicates the size of the traffic flow q_a, and the different hatches represent q_a / c_a, i.e., the amount of vehicle traffic traveling on a road link relative to the capacity of that link and provide estimates of link travel times. A ratio of q_a / c_a of 0.85 or higher indicates congested traffic.

Generation of Traffic Messages

In sub-models 10 (analysis) and 11 (simulation), the fleet of probe vehicles is taken as a certain percentage of all vehicles. Probes are assumed to be equipped with positioning and radio communication equipment. By means of simulation or computation, the effectiveness of the probe vehicle concept is analyzed. The probe vehicles are assumed to generate traffic messages according to the supply or the demand transmission scheme. The transmitted probe messages contain the position of the probe vehicle and the link travel time experienced. The process of generating traffic messages under the demand scheme is rather straightforward. The base station transmits a polling message to one specific probe vehicle in its cell and this probe vehicle responds by transmitting its traffic message. The method to compute the probability of successful transmission is discussed in a separate text in PDF format. When the probe report is successfully received (unsuccessful reception may occur due to path loss, fading or noise) the base station polls the probe vehicle next in turn. Whenever the transmitted probe report is not successfully received, the base stations re-polls the same probe vehicle and may repeat this process a limited number of times before the base station proceeds with the next probe vehicle.

Under the supply scheme, the process of transmitting probe vehicles messages is even more simple as the probe vehicles are assumed to randomly generate traffic messages, without polling messages from the base station and actually without coordination between transmission from other probe vehicles. As a result, mutual interference between messages transmitted from different probes can occur. Messages transmitted in the vicinity of a base station are more likely to capture its receiver than other messages. To study this, the effect of distance on the distribution of traffic messages received by a listening base station is obtained by simulation or by numerical evaluation of an analytical model.

Simulation

The simulation generates probe vehicles sequentially, and it follows probes driving along the calculated shortest routes from their specific origin to their specific destination. Each probe transmits on average once every T seconds, choosing a random time slot. For every transmitted message we store its time slot and, after all probes have been generated and have reached their destinations, the data base with time stamps is sorted on time slot (i.e., a chronological database results containing all transmitted probe messages). Subsequently for each transmitted message, its (area-mean) received power is calculated from a path loss model, as well as the (area- mean) interference power from other messages in the same time slot. To include the effect of fading, a random experiment decides whether the message is received successfully at a base station. The probability of successful reception is in agreement with theoretical models to be described in the next section. This process is repeated for all base stations.

The Promot Menus


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JPL's Wireless Communication Reference Website Marcel Westerman and Jean-Paul M.G. Linnartz, 1993, 1995.