Chapter 1
Effective Information
Sharing based on
Mass User Support
for Reduction of Traffic
Congestion
Tomohisa Yamashita, Kiyoshi Izumi, Koichi Kurumatani
CARC, AIST, Japan
tomohisa@carc.aist.go.jp
kiyoshi@ni.aist.go.jp
k.kurumatani@aist.go.jp
In this research, our aim is to increase the utility of the drivers by reducing traffic
congestion. To attain our purpose, we propose a simple route guidance mechanism
based on route information sharing (RIS). The drivers using the RIS notify their route
to the route information server, and then it returns accumulative information based
on their routes to them. As a result of multiagent simulation, we conform that the
travel time of the drivers using the RIS and others decrease as the drivers using the
RIS increased.
Recently, ubiquitous computing environments in road transportation systems
have developed rapidly; car navigation systems have spread widely, the accuracy
of GPS is constantly advancing, and the range over which the vehicle information
and communication system (VICS) can provide congestion information has been
2
Information Sharing for Reduction of Traffic Congestion
extended [9]. 1 These advances were made possible by the development of invehicle communication devices, sensors, and processors. One of most important
information services in road transportation systems is navigation from an origin
to a destination. Based on such developments, many researchers have been trying
to develop navigation systems, and to examine what kind of traffic information
is variable [1, 5]. However, previous research has revealed that the inprovement
of traffic efficiency by simply providing traffic information is difficult [4, 6, 8].
Usually, navigation systems recommend routes that have shorter distance and
are not congested based on map information and the current traffic state. To
decrease travel time, a driver with a navigation system chooses one recommended
route. However, if other drivers simultaneously choose the same recommended
route, traffic is concentrated on that route. As a result, traffic congestion is
caused on the recommended route. Therefore, traveling the recommended route
often takes longer even though navigation systems recommended it for decreasing
travel time. To make matters worse, traffic congestion increases even more
exponentially as more drivers choose the recommended route. As car navigation
systems have been spreading rapidly, this is a serious problem.
In our work, we introduce the concept of mass user support [2, 3] to avoid the
unintentional traffic congestion caused by use of navigation systems. Mass user
support provides information services to users, or groups, or mass that go beyond
conventional personal services. Although conventional services only considers
individual utility, mass user support considers not only the amount of individual
utility but also interactions among users. The purpose of mass user support is
to increase utility for both individuals and society, but to increase the benefit
to society by sacrificing benefits to individuals. Mass user support requires
some kind of social coordination that leads to mutual concessions among users
[2]. Moreover, a service based on mass user support is required to provide not
only an increase of utility but also fairness among users, stability of the service,
transparency of the mechanism, and robustness against disturbance because
mass user support deals with many users at the same time.
In order to increase individual’s utility and social benefits by reducing traffic congestion, we introduce information sharing of the route that vehicles will
pass in the future through the route information server. This mechanism is expected to lead the situation that the drivers using the RIS know the tendency
of congestion, and then, without concentrating at one route, they voluntarily
and gradually change their routes to avoid area that will be congested. With
multiagent simulation, we examine the effectiveness of our proposed mechanism
from the following two points, i) as the drivers using our proposed mechanism
increases, the efficiency, e.g., travel time from their origins to their destinations,
should increase, ii) the efficiency of the drivers using our proposed mechanism
should always be better than that of the drivers using other mechanisms.
1 The VICS Center [9] collects, processes and edits information about traffic congestion, road
control, and other traffic information in real time, and then provides road traffic information
in words and graphics through communications and broadcast media (FM multiplex broadcasting and beacons). Drivers can receive traffic information via navigation devices installed
in vehicles.
Information Sharing for Reduction of Traffic Congestion
3
Figure 1.1: Direction of movement of vehicles and revision of blocks
1.1
1.1.1
Multiagent Modeling
Traffic Model
In our traffic flow model, a road between intersections is called a link, and is
divided into several blocks [6, 7]. The length of a block is equal to the distance
that a vehicle runs at free flow speed Vf of the link during one simulation step.
After division of a link, an order is assigned to each block from downstream to
upstream. Concerning the block assigned to be the i-th, we define Ki as the
density of block i, Li as the length of block i, Ni as the number of the vehicles
in block i, and Vi as the feasible speed of vehicles in block i. In block i, Vi is
revised based on Greenshield’s V-K relationship, described as
Vi = Vf (1 −
Ki
Kjam ),
(1.1)
where Kjam is jam density, which means the minimum density prevents vehicles
in a traffic jam from moving.
The process of the flow calculation between the two neighboring blocks i and
i + 1 is as follows. At every step, the speed of vehicles in each block is revised
according to the V-K relationship. Based on Vi , vehicle j can move forward
from downstream to upstream as shown in Fig. 1.1. If Ki is exceeds jam density
Kjam , no vehicles can move into block i from block i + 1. At the next step in
block i, vehicles can accelerate or slowdown to revised Vi immediately regardless
of the speed in the previous last step.
1.1.2
Route Choice Behavior
In our simulation, three types of drivers in a route choice are prepared.
Shortest Distance Route
The driver using the shortest distance route (SD) simply chooses the route that
provides the shortest distance from the origin to their destination. It searches
the route based on only map information.
4
Information Sharing for Reduction of Traffic Congestion
Shortest Time Route
The driver using the shortest time route (ST) chooses the route that has the
shortest travel time from an origin to a destination, and revises the route whenever it approaches an intersection. This type of the driver searches a route based
on current traffic information of the entire network that a traffic information
center (e.g., VICS Center) provides.
It considers the expected travel time of a link as calculated based on the
current density of each block. First, speed Vi on block i is calculated based
on the V-K relationship with density Ki . Next, the passage time of block i is
calculated based on length Li of block i and speed Vi on block i. Finally, the
passage time of a link is calculated by summing these of all blocks on the link.
We define expected travel time ET Tl as the summation of the passage times of
all blocks on link l. The drivers using the ST search for the shortest route based
on the expected travel time.
Shortest Time Route with Route Information Sharing
The driver using the shortest time route with route information sharing (RIS)
chooses a route based on current congestion status and accumulative information
about the routes of drivers using the RIS, and revises the route whenever it approaches an intersection. The route information server shares route information
of the drivers using the RIS.
In the RIS mechanism, at first, the driver using the RIS searches the shortest
route from its origin to its destination in the expected travel time. The route
information server collects the routes of all drivers using the RIS, and assigns
passage assurance of a driver to a link, which the degree of assurance that a
driver will pass through a link in the future. Passage assurance P Al,j of driver
j to link l is calculated as follows. If a route passes through p links from driver’s
current position to a destination, the route information server assigns a order to
each link from the destination to driver’s current position on the route. Next,
the route information server divides the order by p and regards it as the weight
of a link.
Based on passage assurance of each link, the route information server calculates the total passage assurance of each link, which means the accumulation of
passage assurance of all drivers using the RIS, and provides it to them. Total
passage assurance T P Al is defined as the sum of the passage assurances of all
drivers to link l. The route information server calculates T P Al as,
k
(1.2)
T P Al = k∈RIS P Al,k ,
where RIS is the set of the drivers using the RIS.
The route information server provides the total passage assurance of all links
for the drivers using the RIS. Finally, the prospective traffic volume P T Vl of
link l is defined as the product of ET Tl and (T P Al + 1.0). The driver using the
RIS searches the shortest route in the prospective traffic volume from its current
position to its destination.
Information Sharing for Reduction of Traffic Congestion
5
Figure 1.2: (a) Lattice network and (b) radial and ring network
1.2
1.2.1
Computer Simulation
Simulation Settings
In order to evaluate our proposed route guidance mechanism, we performed
simulation in the ratio of three types of route choice that the ratio of the drivers
using the SD is fixed at 0.2, and that of the drivers using the ST and RIS
are altered from ST:RIS = 0.8:0 to ST:RIS = 0:0.8. As the structure of road
networks, we use two kinds of road networks, the lattice network and the radial
and ring network in Fig. 1.2. In these road networks, all links had the same
capacity. The origin and destination of a driver were assigned randomly to any
block on any link. After reaching its destination, the vehicle is taken away from
the network. At every simulation step, vehicles were generated until the amount
of vehicles reaches to 25,000. In order to examine the effectiveness under the
situation that roads are not vacant but a deadlock is not caused, the number of
vehicles generated at one step Ngen is set at 45 in the lattice network, and at 35
in the radial and ring network.
The travel time of each driver was normalized by the ideal travel time to
compare our simulation results of the different road networks and the different
sets of origins and destinations of vehicles. The ideal travel time was the time
required from an origin to a destination when a driver passes through the shortest
route for the distance at free flow speed. The travel time was defined as the ratio
of the actual time taken to travel from an origin to a destination and the ideal
travel time.
6
Information Sharing for Reduction of Traffic Congestion
Figure 1.3: Average travel time in the lattice network
1.2.2
Simulation Results
In estimating the results of simulations, we especially focused on a comparison
of the average travel time of the drivers using the SD, ST, and RIS. The results
of our simulation based on the average of 10 trials are shown in Figs. 1.3 and
1.4. In these graphs, the horizontal axis is the ratio of the drivers using the RIS,
and the vertical axis is the average travel time. The ratio of the drivers using the
RIS in all drivers is described as RRIS . The average travel time of the drivers
using the SD, ST, and RIS is described as T SD , T ST , and T RIS .
Fig. 1.3 shows the average travel time in the lattice network. From RRIS =
0.1 to RRIS = 0.3, T SD , T ST , and T RIS slightly increased. After that, all
three decreased as RRIS increased. the average travel time of three types were
always ranked in ascending order as T SD , T ST , and T RIS . There was always
only marginal difference among them. Fig. 1.4 show the average travel time in
the radial and ring network. T SD , T ST , and T RIS decreased as RRIS increased.
The average travel time of three types were always ranked in ascending order
as T SD , T ST , and T RIS . T RIS was always marginally better than T ST . The
difference between T RIS and T SD was from 0.35 to 0.62.
From these results, we observed that, in both networks, i) as the drivers
using the RIS increased, the travel time of the drivers using the RIS and the
other drivers substantially decreased, ii) the travel time of the drivers using the
RIS was better than that of the drivers using other mechanisms. Therefore,
we confirmed that the RIS mechanism was effective on improving the traffic
efficiency of drivers.
Here, we discuss the effectiveness of the RIS based o the difference of two
networks. In the radial and ring network a driver had only one or two shortest
distance routes. Because these shortest ones statistically tended to go through
the innermost ring, the drivers using the SD tended to concentrate at the inner-
Information Sharing for Reduction of Traffic Congestion
7
Figure 1.4: Average travel time in the radial and ring network
most ring. The drivers using the ST and RIS could avoid traffic congestion in the
innermost ring. However, the drivers using the ST often caused traffic congestion by avoiding the innermost ring and concentrating vacant links in the second
and third innermost ring. On the other hand, the drivers using the RIS could
prevent causing traffic congestion in the second and third innermost rings by
route information sharing. Compared with doing in the radial and ring network,
the drivers using the SD did not concentrate seriously at central links in the lattice network because the drivers using the SD had some shortest distance routes
Because traffic congestion tended to be caused also at links other than central
links, the drivers using the ST and RIS had more difficultly to preliminarily
avoid congested links. Accordingly, however the drivers using the RIS increased,
the travel time of the drivers using the RIS did not decrease monotonously, and
the difference of the travel time among the SD, ST, and RIS was small.
In our simulation, the lattice network we use in our simulations was very
symmetrical and ideal, i.e., all links had the same capacity and length, and
there were no one-way streets and traffic regulation. Although we can easily
find the lattice network in New York and Kyoto, there are not so many shortest
distance routes from an origin to a destination, and the links where the number
of passing vehicles is more than traffic capacity, i.e., the bottlenecks, exist certainly. Therefore, lattice networks in practice may has also the characteristics
similar to the radial and ring network that traffic congestion tend to be caused
in certain bottlenecks. In such a network, it is important to prevent two types
of traffic congestion. One is caused in the bottlenecks, and the other is caused
in neighboring links by drivers avoiding first type of congestion and concentrating vacant alternative routes. Because, in the radial and ring network, route
information sharing is regarded as the effective mechanism about preventing
two types of traffic congestion, the RIS mechanism will be effective on reducing
traffic congestion in actual traffic flow.
8
1.3
Information Sharing for Reduction of Traffic Congestion
Conclusion
In this paper, we proposed a route guidance mechanism with route information sharing. Three types of route choice behavior were prepared: the Shortest
Distance (SD), the Shortest Time (ST), and the Shortest Time with Route Information Sharing (RIS). The effectiveness of the RIS mechanism was examined
through multiagent simulation in the lattice network and the radial and ring
network. From the results of simulation, we confirmed that the RIS mechanism
was effective on improving the traffic efficiency of drivers.
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