Vehicular Sensor Networks: General Aspects and Implementation Issues - Application and Multidisciplinary Aspects of Wireless Sensor Networks - page 208

moving with history-based speeds. When reaching the crossroads, the vehicles
randomly choose a new direction, and, contrary to the previous model, the
vehicles can change the lane at crossroads. The security distance is also used,
but there is no control mechanism at crossroads where nodes continue to move
without stopping making the model unrealistic [ 17 ].
City Section Model (CSM) is generated-map-based algorithm, where the nodes
pause time and destination selection are randomly chosen. The speed of the nodes
is constrained by the security distance, along with the maximum speed limit of the
road. Similar features are applied to the Rice University Model [ 18 ], which differs
in using real maps obtained from the TIGER/Lines database [ 18 ].
Stop Sign Model (SSM), contrary to the previous models, integrates a traffic
control mechanism, where in every crossroad there is a stop signal that forces the
vehicles to slow down and pause. SSM is based on real maps of the TIGER/Lines
database, and all roads are assigned a single lane in each direction. Overtaking a
vehicle is not allowed, and the vehicle should tune its speed to keep the security
distance. The problem with this model is the unrealistic disposition of the stop
signals since it is not realistic to expect to find stop signals at each intersection.
Traffic Sign Model (TSM) replaces the stop signals by traffic lights. When the first
vehicle reaches the intersection, the light is randomly turned red with some probabil-
ity p . Then, the vehicle pauses for a random time (pause time). After the delay, the
light turns green and the nodes traverse the crossroads until the queue is empty. When
the next vehicle arrives, the process is repeated. TSM is more stable than SSM since
the pause time for stop signals is shorter than the one for traffic lights [ 19 ].
STRAW is also a model using real maps of TIGER/Line database and like the
other models (except freeway), the roads include one lane in each direction and are
divided into segments. The nodes are placed randomly at the beginning of the simu-
lation, then they are moving using the car following model [ 20 ] and are accelerating
until reaching the maximum speed of the segment. The overtaking is not allowed,
but the security distance is maintained. At crossroads, the vehicles always slow
down, even when they change a segment and they turn without a full stop, which is
realistic. The traffic control mechanism defines the stop signs and traffic lights, and
the routes are selected either randomly or toward a path that uses the shortest lane.
MOVE [ 21 ] is a tool built on top of an open source micro-traffic simulator named
SUMO [ 22 ]. It incorporates GUIs to facilitate the process of road topology and
vehicular mobility definition. The output of MOVE is a mobility trace file that con-
tains the information of vehicle movements, which can be used by various simula-
tion tools. MOVE consists of Map Editor and Vehicle Movement Editor. The first is
used to create the road map, which can be created manually by users, automatically
generated, or imported from existing real world maps. The second allows users to
specify the trips of vehicles and the route that each vehicle will take for the trip.
A novel and realistic model is proposed by Gorgorin et al. in [ 23 ], where an
overtaking mechanism is applied within multilane segments. Moreover, the model
allows specifying the driver type, which affects many parameters of the vehicle,
like speed and acceleration. The model includes traffic lights and stop signs at the
intersections.