Evaluation of Information Dissemination Characteristics in a PTS VANET Holger Kuprian 1, Marek Meyer 2, Miguel Rios 3 1) Technische Universität Darmstadt, Multimedia Communications Lab Holger.Kuprian@KOM.tu-darmstadt.de 2) AGT Group (Germany) GmbH mmeyer@agtgermany.com 3) Pontificia Universidad Católica de Chile mrios@ing.puc.cl Abstract-- This work examines the feasibility of using a vehicular ad hoc network (VANET) to improve the bus location monitoring and control of the public transportation system (PTS) in Santiago, Chile. A realistic simulation, based on geo data of Santiago and bus route information obtained from the Transantiago PTS, provides results about the impact of different transmission ranges of vehicles and roadside units (RSU) and the necessity of RSUs to support the infrastructure and performance of VANETs. Different traffic situations are studied by varying the amount of vehicles used in the simulation. Simulation results state that available MANET routing protocols provide similar suboptimal results for the presented VANET scenario. This presents a strong requirement for VANET routing protocols to be used in high mobility environments. The more RSUs are deployed, the higher is the delivery ratio and the lower the mean end-to-end delay and path length. In contrast to the deployment of RSUs, the highest transmission range did not lead to the best results. Simulations conducted with transmission ranges of 800m and 1000m achieved worse results than a transmission range of 500m. Index terms Public Transportation, VANET, Vehicular Ad hoc Networks, Santiago de Chile I. INTRODUCTION Vehicular ad hoc networks consider vehicle-tovehicle (V2V) and vehicle-to-infrastructure (V2I) communications, to support common goals in intelligent transportation systems (ITS), such as safe driving, dynamic route scheduling, emergency message dissemination and traffic condition monitoring [1,2]. This work examines the applicability of vehicular ad hoc networks in the public transportation system in the city of Santiago, Chile. Therefore, requirements for a VANET for public transportation systems are analyzed and, based on the results, a realistic simulation of movements and communication behavior of buses in Santiago is implemented using the simulation environment OMNeT++, including original geo data of Santiago and the exact modeling of bus routes. The simulation investigates the necessity of improving the performance of roadside units (RSU) to be able to use a VANET in Santiago, and to establish a desired service availability (e.g. knowledge about buses location for at least 90 percent of the time). Furthermore, the simulation results include the behavior of dissemination delays and percentage of dropped messages based on the different values for input parameters such as transmission ranges of buses and RSUs and number of vehicles in the simulation. To intercommunicate buses and RSUs, routing protocols are needed. The available routing protocols in the OMNeT++ package (i.e. the already implemented protocols) are studied with respect to their influence on the crucial result parameters (drop percentage, end-toend delay) and with respect to the overhead they produce, i.e., the amount of messages they need to route through the network. Although their performance has been studied before [3], the area of VANETs is new and the application to public transportation systems has not been examined before. In addition, the routing protocols are compared to a simple flooding approach. II. MANETS AND VANETS A mobile ad hoc network is a type of wirless network that forms a network without infrastructure typically for a limited amount of time [5]. The big challenge is to enable communication between participating nodes despite mobility and changing topologies. The simplest ad hoc network is a set of computers connected (via cable or wireless) to form a small network. Ad hoc networks are also in use for connecting Bluetooth devices such as a mobile phone and a headset. The most important attributes
of a MANET are self-organization, node mobility and scalability. Vehicular ad hoc networks are an enhancement of mobile ad hoc networks. If the nodes of a MANET are placed inside a vehicle it is called a VANET. While VANETs and MANETs have a lot in common, the greatest difference is the degree of mobility, since the nodes (i.e. vehicles) in a VANET typically move much faster than the nodes in a MANET. This leads to faster changing topologies, which are challenging to handle. III. SIMULATION SCENARIO AND PARAMETERS The public transportation system (PTS) of Santiago consists of one metro system and ten bus operators, which are each responsible for provide the service to a particular sector of the city. For simulation purposes, we have selected one representative sector out of the ten. The simulation uses an original road map of Santiago de Chile, which has been extracted from OpenStreetMap. The bus traffic within this sector has been modeled acording to the bus routes time tables at the rush hour. Several different scenarios were studied whose variable parameters included: Different MANET protocols Different wireless transmission range Variable number of RSUs Variable number of buses With each of these scenarios, the simulation performance measures used for evaluation were: Packet delivery ratio Mean end-to-end delay Mean hop count Routing errors sent Routing requests sent Routing protocol overhead Traffic overhead Figure 1, shows the interconnection of all data and processes. The simulations were executed on four equal desktop computers with the following specifications: Intel Core 2 Quad CPU Q9400 with 2.66GHz and 4GB RAM. The mode of operation for the simulation was as follows: Every 30 seconds, all buses send a location update message to the operations control center (OCC). Since the RSUs are already connected with the OCC, the buses will search for the five closest RSUs and send the update messages to them. The routing protocol handles the multi-hop message delivery. The five messages sent by the bus count only as one message for the statistical evaluation, since only one update message is sent and the implementation depends on the application design. RSUs send a management update message to the buses every 30 seconds. This is implemented with a broadcast message to all buses and does not involve any routing protocol. RSUs receiving broadcast messages forward them too. The broadcast only counts as one sent message as well. Multiple arrived messages are filtered both at the RSUs and at the buses to make sure that the maximum delivery ratio is 1. Figure 1. Simulation environment and processing. The simulation time consists of a startup phase of 3000 seconds, where no statistics are recorded. Bus routes take up to 2700 seconds to be completed and given the bus frequencies, it takes this amount of time until all buses following a single road are present in the simulation and an equilibrium regarding the average amount of buses is reached. After the startup phase the simulation is executed for 3600s (1 hour). The amount of buses for an 8000 seconds run indicates that an equilibrium regarding the number of buses is reached after about 2800 seconds. The computation time, depending on the applied routing protocol, number of buses and roadside units, took between five hours and three days. The number of nodes in the simulation consists of mobile buses, which choose their roads according to predefined bus routes and fixed roadside units. The amount of buses is determined by their frequencies during the morning rush hour (6:30am to 8:29am) and is altered with a fixed multiplier of 0.5, 0.66, 1, 1.5, 2 and 3 resulting in quantities of 66 to 562 buses (e.g. a multiplier of 0.5 means that
the bus frequencies are halved resulting in a doubled amount of buses in the simulation). The maximum vehicle speed, as defined for the standard vehicle, has a maximum speed of 72km/h (20m/s) and as measured during simulation an average speed of 30km/h (8m/s). The number of RSUs is varied between one and 100 in steps of ten. The maximum of 100 RSUs was chosen, since with this number nearly the whole road length of the bus routes is covered, given an average transmission range of 500m. One RSU is the minimum, since it represents the operation control center that receives the messages and at least one receiving node is necessary. The simulation is based on an underlying map of a part of the city of Santiago de Chile. The area F of the Transantiago PTS is chosen due to its characteristics of low traffic density (it is a sector with lower population density), thus challenging the characteristics for the establishment of a VANET. This provided a lower bound for areas with higher traffic densities. To summarize the simulation runs, an overview of the simulation parameters used is given in table 1. Table 1.: Overview of simulation parameters Parameter type Parameter value Simulation time 3000s startup phase + 3600s simulation Size of simulation map 11190 x 10530m Routing protocols AODV, OLSR, DYMO-FAU, DYMO-UM Message sending 30s frequency Transmission ranges 250m, 500m, 800m, 1000m # RSUs 1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 MAC protocol 802.11g Bandwidth 11Mbps Car following model Safe Distance Model by Krauss Maximum vehicle speed 20m/s (72km/h) Average vehicle speed 8m/s (30km/h) Avg. # of vehicles 66 562 The packet delivery ratio (PDR) peaks with a TR of 500m, while 250m, 800m and 1000m are significantly worse. With the increase from 10 to 30 RSUs, the PDR rises slightly. We chose DYMO- UM and a transmission range of 500m for further simulation runs. Figure 2. PDR for varying TR and no. of RSUs Figure 3 depicts the results for variable numbers of buses and RSUs, used in the simulation with a transmission range of 500m and the routing protocol DYMO-UM. The mean number of buses was between 66 (one third of the amount during rush hour) and 562 (twice the amount during rush hour). The packet delivery ratios evolve nearly in parallel for all numbers of RSUs. The scenario with 66 buses achieves the worst delivery ratio, while the scenario with 378 buses dominates in all simulation runs with less than 50 deployed RSUs. With 50 or more RSUs, the original configuration with a number of buses that equals those of the rush hour (i.e. 211) performs best. IV. DISCUSSION OF SIMULATION RESULTS As a first step it was evaluated which of the available routing protocols AODV, OLSR, DYMO- FAU and DYMO-UM would be the best to use. The routing protocols were evaluated varying the amount of roadside units and the transmission range. The following combinations of RSUs and transmission ranges (TR) were chosen: 10 RSUs with a TR of 250m, 30 RSUs with 250m, 40 RSUs with 500m, 70 RSUs with 500m and 100 RSUs with 1000m transmission range. The results show that in general, all routing protocols are quite similar for the presented simulation scenario; no protocol dominates in any given setup (Figure 2). Figure 3. PDR for varying number of buses The analysis of the results of different number of buses, shows that more buses do not enhance the performance of a VANET as much as allocating additional RSUs. However, this may also depend on the driving behavior of the additional vehicles as well as on the capability of the applied routing protocol to efficiently use the higher number of
RSUs. Additionally, the end-to-end delay increases significantly, which leads also to the necessity of finding a better VANET routing protocol. Figure 4 shows the results of delivery ratios for all combinations of numbers of roadside units and transmission ranges. As you can easily notice, a TR of 500m achieves by far the best results for all numbers of RSUs, peaking with 100 RSUs at 81% delivery ratio. due to waiting times for new route discoveries, which can be seen in figures 5 and 6. Considering the system high mobility, a high delay increases the probability that the destination vehicle cannot be reached due to fast location and topology changes. With a large transmission range more vehicles are covered by the wireless signal, which increases the possibility of collisions (packet loss) during transmission. The packet loss caused by transmission failures could be the reason for a high amount of routing errors in the first place, since the need for retransmissions increases the latency as well. The exceptionally good performance of a TR of 500m could be based on a balance between middle TR (better than 250m) and less packet losses due to collisions during transmission (better than 800m and 1000m). Figure 4. Packet delivery ratio for DYMO-UM. Figure 6. Mean hop count for DYMO-UM. Figure 5. Mean E2E delay for DYMO-UM. In general, the transmission at all TRs rise with increasing numbers of RSUs. Remarkable is the special poor performance of a TR of 1000m, whose results are the worst for all numbers of RSUs. This poor result is probably affected by the implementation of the radio transmission model. Since we used the radio transmission model of 802.11a with adapted transmitter power, the transmission behavior is not modeled realistically. As stated by Gukhool and Cherkaoui [4], the percentage of packet loss is 50 percent higher at transmissions ranges over 1000m with 802.11a than with 802.11p. Furthermore, the low performance could be caused by a high amount of routing errors [5] or an extraordinarily high use and therefore collisions in the transmission medium. The amount of routing errors increases heavily for TRs of 800m and 1000m with 20 RSUs and is 600 times higher than the configuration that uses a TR of 500m. A high amount of routing errors increases the latency Figure 6. Routing errors sent for DYMO-UM. Figure 7. Routing requests sent for DYMO-UM.
Figure 8. Routing overhead for DYMO-UM. V. CONCLUSIONS The results of our silmulation of a Santiago PTS VANET show that a VANET based location monitoring and control systen is in principle feasible, but the performance varies significantly for some parameters. The tested MANET protocols are not optimal; a dedicated VANET protocol that uses geographic routing or delay tolerant network mechanisms would probably perform better. For the given scenario and parameters, good results are only achieved by deploying roadside units but this implies additional infrastructure costs. Thus, a focus for further experiments is to minimize the number of roadside units by chosing the right parameters. The transmission range has also an impact for out settings, a value of 500 meters performed best. This work examined the feasibility of using a vehicular ad hoc network to improve the bus location monitoring of the public transportation system in Santiago and thereby improving the imperfect knowledge of vehicle location. A requirement analysis for a VANET in Santiago was conducted and the results used as input for a simulation scenario. A realistic simulation based on geo data of Santiago and bus route information obtained from the Transantiago PTS, provided results about the impact of different transmission ranges of vehicles and roadside units and the necessity of RSUs to support the infrastructure and performance of VANETs. Different traffic situations have been studied by varying the amount of vehicles used in the simulation. The conducted simulations in OMNeT++ show very similar results for available MANET routing protocols for the presented VANET scenario. Additional simulations, executed with the DYMO- UM routing protocol, which is based on experiences with the AODV protocol, as a representative for all other protocols, stated the positive influence of RSUs on the most critical performance metric delivery ratio. The more RSUs are deployed, the better the PDR is. Obviously, this is limited by the costs of the deployment and maintenance of each RSU. In contrast to the deployment of RSUs, the highest transmission range did not lead to the best results. Transmission ranges of 800m and 1000m probably suffer from higher packet loss in the wireless medium and especially the TR of 1000m requires an implementation of the protocol standard 802.11p. Higher amounts of buses do not support the VANET as good as a higher amounts of RSUs. More buses lead to significantly higher E2E delay, which could be justified by the higher mean path length and the lacking capability of the applied routing protocol to make use of the higher amount of buses efficiently. References [1] J. Bernsen and D. Manivannan. Unicast routing protocols for vehicular ad hoc networks: A critical comparison and classification. Pervasive and Mobile Computing, vol. 5, pages 1 18, 2009. [2] H. Karl, A. Willig. Protocols and architectures for wireless sensor networks. John Wiley and Sons, 2005. [3] Mun J. Lok, Bilal R. Qazi, and Jaafar M. H. Elmirghani. Characterisation of data dissemination in vehicular ad hoc networks in a city environment. In IWCMC 09: Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing, pages 1299 1303, New York, NY, USA, 2009. [4] B. S. Gukhool and S. Cherkaoui. IEEE 802.11p modeling in ns-2, 2008. Local Computer Networks, 2008. LCN 2008. 33rd IEEE Conference on. [5] H. Karl and A. Willig. Protocols and architectures for wireless sensor networks. John Wiley and Sons, 2005.