Minimum-cost Implementation of Traffic Information System over Wireless Mesh Network Kaveh Shafiee and Victor C. M. Leung Department of Electrical & Computer Engineering The University of British Columbia Vancouver, BC, Canada V6T 1Z4 E-mail:
[email protected],
[email protected]
Abstract—As the traffic situations on highways and streets become more variable and more complex every day, the deployment of a traffic information system (TIS) becomes inevitable. In this paper, we develop an advanced wireless mesh architecture for TIS (WMTIS) which provides vehicles with the least delay routes to their destinations. The contributions of the paper are twofold. First, we propose an efficient communication framework for a TIS based on wireless mesh networking, i.e., WMTIS, and shed light on the mechanisms and the functional elements of WMTIS that are required to be deployed over the wireless mesh topology in order to support traffic applications. The performance improvements of WMTIS over fully ad-hoc TISs are verified via simulations. Second, we propose an approach to minimize the deployment cost of WMTIS while keeping the round trip delays of traffic information messages below the maximum tolerable delay in traffic applications. We use simulations to demonstrate the effectiveness of the proposed cost minimization approach.
I.
INTRODUCTION
Traffic conditions on highways and streets are becoming more variable and congested. Traffic congestions increase the number of accidents, travelling delays and fuel consumption. Traffic information systems (TISs) aim at providing timely information to drivers as a means to balance the vehicular traffic on streets and highways. Ultimately this serves the purposes of utilizing the capacities of roadways efficiently and consequently enhancing safety and reducing the travelling times and the waste of energy. Classical TISs employ infrastructure sensors or video cameras that are installed on the roadsides to send traffic data via a backhaul network to a central traffic monitoring center (TMC), where the data are interpreted to generate traffic advisory messages, which are then broadcast to vehicles via roadside signs or radio broadcast systems. However, the very low capacity of traffic data dissemination in such systems can only cater to either general traffic information over a broad area or specific traffic information at limited locations. However, future TISs should provide location-specific traffic information over all major streets and highways. This work was supported in part by grants from the Nokia University Relations Program, from AUTO21 under Canadian Network of Centers of Excellence Program, and from the BC-China ICSD Program.
To address the limitations of existing TISs, ad-hoc communications, called inter-vehicle communications (IVC), have been proposed for TISs where the traffic information messages are transmitted directly between vehicles without having to go across the backhaul network [1]-[3]. However, these fully ad-hoc TISs suffer from large delays when the traffic information is spread over long distances, e.g., the delay may exceed 25 minutes over a distance of 50 km [1]. This is due to multi-hop ad-hoc communications in which the only way the traffic information is being forwarded is via intermediate vehicles in a fairly slow hop-by-hop manner. What exacerbates the delays is that in forwarding traffic information every vehicle gives priority to the traffic information of its neighborhood over the traffic information which does not concern its immediate neighboring area and is only taking advantage of the vehicle as a relaying node for forwarding to areas far away from its origin. The reasoning behind this priority mechanism is that the accuracy of the traffic information that vehicles need in the network is distance-dependent in the sense that drivers require more details about the traffic status in their vicinity compared to areas far away in their decision-making. Considering these large delays, if no infrastructure is used in TIS to propagate the traffic information over long distances, drivers may have already made their route-selection decisions when they become aware of the traffic situations along the selected route. To deal with the above problem, in this paper we propose the deployment of a wireless mesh TIS (WMTIS), in which cellular vehicle-to-infrastructure (V2I) and ad-hoc vehicle-tovehicle (V2V) wireless communications coexist, to form the communication framework for the TIS. The wireless mesh framework supports multi-hop V2V ad-hoc communications over short distances, and collects the traffic information by V2I communications to base stations (BSs) located sparsely along roadsides for backhaul transmissions to the TMC. This architecture provides timely information dissemination within local areas, while ensuring cost-effective deployment of the communication infrastructure. While direct radio coverage of a road segment by a BS would minimize the delay of traffic information dissemination within the coverage area, deployment of BSs to cover all segments of major roadways would be prohibitively expensive.
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The wireless mesh framework has been already proposed for other vehicular applications in the literature. A wireless mesh network architecture that integrates IEEE 802.11 access technology with ad-hoc communications is introduced in [4], which specifically aims at providing broadband wireless access (BWA) for vehicles. Another mesh architecture including IEEE 802.16 access technology and ad-hoc communications is studied in [5] to support safety applications and Internet access in which the ad-hoc communications among vehicles are via IEEE 802.16 air-interface cards. The authors of [6] focus on IEEE 802.16e access technology and integrate it with ad-hoc communications to introduce a mobility pattern routing protocol for vehicular networks by equipping the vehicles with both IEEE 802.11 and IEEE 802.16 wireless interfaces. None of these studies discuss the mechanisms and the functional elements that are needed for efficient implementation of a WMTIS. To the best of our knowledge, this paper is the first work that characterizes the essential components and the logical relations that should be deployed over wireless mesh topology to support traffic applications (Section II). After presenting the WMTIS, we develop an approach towards reducing the cost of the infrastructure by minimizing the number of BSs in the system in Section III. Although some methods for optimizing the placement of BSs in a mesh topology already exist in the literature [7]-[9], none of these methods consider the BSs’ placement optimization with respect to maximum tolerable delays in traffic applications. In Section III the BSs’ placement optimization is achieved by taking the maximum tolerable delays experienced in the TIS into account. Finally, Section IV concludes the paper. II.
THE WIRELESS MESH TIS (WMTIS)
A. Assumptions and functional components In most existing work addressing vehicular networks for future intelligent transportation applications [10, 11], it is assumed that vehicles are all equipped with global positioning system (GPS) receivers for accurate positioning, in addition to digital radios for wireless communications. We shall apply the same assumptions in this paper. Vehicles periodically send beacon messages to report their positions and velocities to surrounding vehicles and based on these beacons they maintain an accurate neighbor list in their look-up tables. We assume that the BSs in the WMTIS are interconnected through a highspeed backhaul network that introduces negligible communication delays compared to the wireless V2V and V2I communications. The V2V and V2I communications could employ any appropriate wireless standard, such as WiFi, WiMax or dedicated short range communications (DSRC). The WMTIS framework we propose is general in nature and independent of specific radio technologies. An important component of the WMTIS is the TMC to which all the BSs are connected via the high-speed backhaul network. The BSs send all the packets they hear from vehicles to the TMC and the TMC keeps track of this real-time traffic information in all parts of the network for analysis and advisory dissemination. In order to make the TMC scalable, we could consider several TMCs in a hierarchical structure. This
Fig. 1 A typical scenario where a vehicle sends a request for the TMC via a BS
approach, however, is outside the scope of this paper, and throughout the paper, we only consider one TMC capable of handling all the traffic information storing and processing. B. Traffic information request and reply mechanisms When a vehicle needs the route with the minimum delay to its destination, it sends a request packet to the TMC via a BS. The mechanism is that the request-generating vehicle forwards the request packet with the help of its surrounding vehicles in the network and the request packet traverses across the network until it is received by a BS. We illustrate the details of this mechanism by the example given in Fig. 1. R and r are the transmission ranges of V2I and V2V communications, respectively, which may be different. The request-generating vehicle (vehicle A) forwards its request packet in both directions. On the left hand side, the request packet is forwarded to the BS through vehicles C and E in steps 1 and 2, respectively. The numbers under the vehicles or the BSs specify the steps in which they forward the corresponding packet. The forwarding procedure is that the vehicle responsible for relaying the packet forwards the packet to the farthest vehicle in its look-up table in the forwarding direction. We adopt an implicit acknowledgement mechanism in the sense that the forwarding vehicle considers the retransmission of the same packet by the next relaying vehicle as the acknowledgement. This mechanism is more efficient than explicit acknowledgement mechanisms. After receiving the request packet in step 3, the BS carries out two separate tasks. First, in order to prevent vehicle G from forwarding the packet further ahead, it immediately sends an acknowledgement packet. Vehicle G stops forwarding the packet when it hears the acknowledgement packet broadcasted by the BS. Second, the BS forwards the request packet to the TMC and the reply packet generated by the TMC is sent back to the vehicle through the BS in step 4. On the right hand side, after being forwarded by vehicles B, D and F, the request packet is received and discarded by the BS, because it is found repetitive. Since the TMC has real-time information at any point in time, the delay problem present in fully ad-hoc TISs, i.e., large delays incurred by multi-hop propagation to long distances which could result in obsolete traffic information, is avoided.
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sequence of APs that leads to the route with the minimum delay for any request it receives. The proposed grid layout approach is applied to a typical scenario in Fig. 4. Source and Destination denote the initial position of the vehicle and its final destination, respectively, and the black ‘X’s are the temporary destinations (TDs) which are the closest possible points on the streets to the APs in the AP sequence generated by the TMC. In this scenario, for 2km×2km squares in the grid layout, the AP sequence is APs A, B, …, G, and the dashed-line is the end-to-end route the vehicle goes along. Note that the consecutive APs in the AP sequence could be vertically, horizontally or diagonally adjacent APs. C.
Fig. 2 2km×2km grid layout on a typical map
Fig. 3 4km×4km grid layout on a typical map
One of the critical challenges in WMTIS is the logic the TMC employs to generate the reply packets. Although the TMC is aware of all the traffic details about every vehicle in the network, if it factors all of these details in its minimum delay route calculations and includes every single intermediate street segment in the reply packets it generates and sends back to vehicles, the system will be inefficient in terms of bandwidth and computing resources. Instead, WMTIS considers a grid layout on the map and defines some virtual points, called anchor points (APs), at the centre of the squares in the grid layout. The APs are also known to all vehicles. Different scales for the grid layout are possible in the sense that an AP can represent squares of different sizes. The 2km×2km grid layout and 4km×4km grid layout are for instance shown on a typical map in Figs. 2 and 3, respectively. The APs are represented by blue ‘X’s in the figures. The TMC, on the basis of the real-time traffic information it receives from BSs, continuously calculates the average times that vehicles take to drive across any of the squares in the grid layout. Based on these average delays, the TMC computes the
Evaluting the performance of WMTIS In this section, we establish a simulation scenario to investigate the improvements WMTIS makes over fully ad-hoc TISs, and to study how the performance is affected when different scales in the grid layout approach are used. We compare WMTIS with self-organizing traffic information system (SOTIS) [3] and Traffic View [2] which are two plausible representatives of ad-hoc TISs. In SOTIS each street is divided into several segments, and vehicles send the average velocities of both the segments in their transmission ranges and other segments periodically. While 66% of the traffic packets, i.e. the average velocities a vehicle disseminates are allocated to the segments in its range, the rest concern the segments in a larger surrounding (50 - 100km away). As mentioned earlier, the unequal allocation is because the details that vehicles need on traffic situations are distance-dependant. TrafficView has a mechanism similar to SOTIS, but instead of the average velocities in segments, it disseminates the positions and velocities of individual vehicles periodically. Unlike WMTIS that provides drivers with the minimum delay routes to their destinations, ad-hoc TISs such as SOTIS or TrafficView only provide drivers with traffic information such as average velocities or average vehicle densities on streets and it is up to drivers to make decisions and select their routes. The decision-making mechanism drivers employ could for instance be the selection of the least crowded street or not selecting the most crowded one on a Google Maps type of interface in which different average velocities on the streets, for example, could be differentiated by different colors. Since in ad-hoc TISs the traffic information of an area becomes less accurate as the distance from that area increases, in this paper we develop a decision-making mechanism with respect to the area in the vicinity of vehicles. We define a rectangular area of a limited size called the zone-of-relevance (ZoR). Every vehicle selects a point on the map closest to its final destination as its temporary destination (TD) in a way that both the vehicle and the selected point lie within a ZoR. Then, on the basis of the available traffic information provided by the employed ad-hoc TIS, the vehicle finds the shortest path in the ZoR to the TD in terms of the time it takes to get there. The shortest path is obtained by mapping the junctions and streets of the ZoR onto the vertices and edges of a graph and applying the Dijkstra’s algorithm to the graph when the average delays on the streets are considered as the weights of the corresponding edges. Once the vehicle gets to the TD, the same mechanism is used again to obtain the next
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(Fig. 4) is different from the end-to-end route the simultaneous employment of SOTIS and decision-making mechanism suggests (Fig. 5). The reason is that the TMC has accurate realtime traffic information about the status of the highway (the thicker line) in the map while there is no guarantee that SOTIS informs vehicles about the traffic status on the highway at the right time.
Fig. 4 Applying the grid layout approach to a typical scenario
Fig. 5 Applying the decision-making mechanism to the same scenario in Fig. 4
TD and this process continues until the final destination is reached. In Fig. 5 the decision-making mechanism is applied to the same scenario in Fig. 4 when the traffic information is obtained by SOTIS. Again, the dashed-line is the selected endto-end route. It is clear that the smaller the size of ZoR, the shortest path is computed more accurately, because the accuracy and up-to-datedness of the traffic information provided by ad-hoc TISs are distance-dependent, but fewer options, i.e., fewer streets are taken into consideration as well. On the other hand, when the vehicle is located in an area with densely distributed streets and highways, e.g., urban streets, a ZoR with a larger size makes a remarkable improvement, compared to an area with sparsely distributed streets and highways, e.g., suburban highways. Furthermore, when the decision-making mechanism cannot find any route to the TD in the ZoR, it should adaptively increase the size of the ZoR. The adaptive determination of the size of ZoR constitutes the theme of our next work. By considering Figs. 4 and 5, we observe that for the same source and destination the end-to-end route WMTIS suggests
The map is derived from a real street map in TIGER database [12] from US Census Bureau. In the simulation scenario, we choose an 8km×8km area with an average street length of approximately 500 meters on the map and export the selected area to the simulation of urban mobility (SUMO) [13] which is a microscopic road traffic simulation package in which we define the maximum velocity and the priority of usage for every street in the area. Then, different flows of vehicles are injected into the area to achieve different average vehicle densities in the zone. In the next step, the mobility model created by SUMO is given to the mobility model generator for vehicular networks (MOVE) [14] as an input to generate a mobility trace file containing the movements of vehicles which can be immediately used by network simulator 2 (NS-2) [15]. All the parameters we used in the simulations including those related to the mobility model and the wireless communications system are listed in TABLE I. Every vehicle randomly selects one of the edgeintersections of the simulation area as its final destination and during the simulation time when a vehicle gets to one final destination, another edge-intersection is randomly selected as the next final destination. All of the final destinations that every vehicle selects during the simulation time are saved as a log. Vehicles go to the destinations recorded in their logs one time by using Traffic View, one time by using SOTIS and another time by using WMTIS (one time for any of the grid layout scalings). The average travelling delays of vehicles in the network to their destinations versus average densities of vehicles are shown in Fig. 6. Each result was obtained by taking the average value from 30 simulation runs. It is worth mentioning that the improvement WMTIS makes increases as the density of vehicles increases. III.
REDUCTION IN THE INFRASTRUCTURE COST
Up to now, we assumed that BSs cover all parts of the network directly. However, depending on the average vehicle densities, some areas may not need to be covered directly by BSs, but via vehicles in a multi-hop manner. In this section we obtain the maximum allowable inter-BS distances, which result TABLE I.
MOBILITY-RELATED AND WIRELESS COMMUNICATIONRELATED PARAMETERS USED IN THE SIMULATION
Average velocity Average vehicle density Simulation time r (vehicles’ transmission range) R (BSs’ transmission range) MAC layer Max. Contention Window Data rate Beaconing frequency Beacon size Radio Model
15 ~ 105 km/h 0.0014 ~ 0.0054 veh/m per lane 10000 sec 200 m 400 m IEEE 802.11 DCF 32 1 Mbps 2 beacons/sec 512 bits Two Ray Ground
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Average delay form the vehicle to its final destination (sec)
950 900
Fully ad-hoc TIS (TrafficView) Fully ad-hoc TIS (SOTIS) WMTIS (4km * 4km grid layout) WMTIS (2km * 2km grid layout)
850 800 750 700 650 600
40 (1.4)
50 (1.7)
60 70 80 90 100 110 120 130 (2) (2.3) (2.7) (3) (3.3) (3.7) (4) (4.3) The average number of vehicles in the 2.5km*2.5km ZoR (The average density of the vehicles (veh/km per lane))
140 (4.7)
Fig. 6 Average delays vs. average densities of vehicles for SOTIS and WMTIS with different grid layout scalings
communications while e-ρr is the probability that the vehicle carries the packet itself. If the packet is being forwarded via wireless communications, (x / r) and (Res / r) are the number of hops that are needed to cover distances x and Res respectively, which constitute the forwarding delays when multiplied by dhop. Note that the reason we can use the division of the distance by transmission range r to approximately obtain the number of hops is a direct result of our packet forwarding mechanism in which the forwarding vehicle delegates the packet forwarding responsibility to the farthest vehicle in its transmission range. On the other hand, (x / vg) and (Res / vr) are the delays the packet experiences when it is carried along distances x and Res, respectively. The first and the second lines of (1) correspond to the time the request packet goes from the vehicle to the BS and the time the reply packet is sent back from the BS to the vehicle, respectively. One of the parameters that need to be determined in (1) is dhop. In [16] the performance of the IEEE 802.11 DCF when the traffic is uniformly distributed is analyzed for the nonsaturated case and dhop is characterized with high accuracy between two very close upper bounds and a lower bound, as depicted in Fig. 8. The lower and upper bounds are obtained by using queuing theory [17] and considering a general distribution for the arrival rate of packets, i.e., a G/G/1 system, as follow [16]:
E[Ts ] ≤ d hop ≤ 2 E[Ts ]
d hop ≈ E[Ts ] + E[ R] ≤ E[Ts ] +
E[Ts 2 ] ≡ TUR 2 E[Ts ]
(3)
where Ts is the service time and p in the figure is the probability that a node’s transmission encounters a collision and is equal to:
Fig. 7 Two consecutive BSs with distance d = 2x + 2r
in the minimum required number of BSs in WMTIS, versus different given maximum tolerable delays for different average vehicle densities. For this purpose, we consider a scenario in which two consecutive BSs are located d = 2x + 2r away from each other where r is the transmission ranges of V2V communications and x ≥ 0 (Fig. 7). If we assume that a vehicle is equidistant from the BSs, it statistically goes through the maximum delay when communicating with the BSs. The time it takes the vehicle to send a request packet to one of the BSs, e.g., the one on the right hand side, and the reply packet to be sent back to the vehicle, called round-trip delay (RTD), depends on the average vehicle density and the average velocities on the street and the average delay per hop (dhop). If we assume that the arrivals of vehicles on the street is Poisson, which is a common assumption in the relevant studies, then the inter-vehicle distance on the street with average vehicle density ρ has an exponential distribution with average 1 / ρ. Therefore, RTD can be written as: R T D = (1 − e − ρ r )( x / r ) * d h o p + e − ρ r ( x / v g ) + d h o p + d h o p + (1 − e − ρ r )( R es / r ) * d h o p + e − ρ r ( R es / v r )
(2)
(1)
where vg and vr are the average vehicle velocities in the forward and returning directions, respectively. (1 – e-ρr) is the probability that the packet is forwarded via wireless
p = 1 − (1 − pt )n − 1 (4) where n is the number of the nodes that are contending for the wireless media and can potentially cause collision with each other, and pt is the transmission probability of each node in any time slot, which depends highly on the packet traffic of the network. As it is observed in Fig. 8, if p is kept smaller than or equal to 0.1, dhop will remain below 30 ms. Based on this observation, we define another simulation scenario to obtain RTD versus x. The simulation area is the same as the one in Subsection II.C, and we impose all the assumptions in [16] to this scenario. For every street in the simulation area, based on the average density of vehicles on the street, the average value of n is calculated. By taking the calculated n into account and using (4), the maximum background packet traffic in the network that yields p = 0.1 is obtained which is necessary to make sure dhop remains below 30 ms. The background packet traffic in the network is constantly kept below the calculated values. We place BSs along the roadsides with different inter-BS distances x ranging from 0 m to 600 m and in each case the requestgenerating vehicle is located between any two adjacent BSs at equal distances from the BSs to obtain the RTDs. The average RTD for the vehicle versus x when all the adjacent BSs in the network are taken into account is obtained one time via
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deployed BSs in the network, and the maximum tolerable delays have been studied. V. [1]
[2]
[3]
Fig. 8 Upper and lower delay bounds for dhop
[4]
[5]
RTD (ms)
[6]
[7]
[8]
[9]
[10]
Fig. 9 The RTD for different average vehicle densities using both simulation and analysis
simulations and another time through analyses by employing (1) (see Fig. 9). As it can be observed in the figure, the results of the simulations agree well with the results of the analyses. Each data point in the simulation results is obtained by taking the average value from 30 simulation runs and the rest of the parameters are the same as in TABLE I. Based on Fig. 9, for any given average density, the maximum x and therefore the maximum d = 2x + 2r can be determined when the upper limit of the RTD is specified. IV.
[11]
[12] [13] [14] [15] [16]
[17]
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CONCLUSION
In this paper, an advanced and efficient WMTIS has been developed on top of a wireless mesh framework, which provides vehicles with the most recently updated end-to-end routes with the minimum delays to their destinations. The simulation results demonstrate the effectiveness of the proposed WMTIS in terms of the average travel times of vehicles in the network, which is a benchmark for assessing the efficiency of TISs with respect to vehicular traffic balancing and congestion avoidance. An infrastructure cost minimization solution with respect to traffic applications has been proposed and the tradeoffs between the maximum possible inter-BS distances, which correspond to the minimum number of
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