models and methods for the optimal location of traffic sensors and vmss a. sforza
DESCRIPTION
MODELS AND METHODS FOR THE OPTIMAL LOCATION OF TRAFFIC SENSORS AND VMSs A. Sforza DIS - Università di Napoli “Federico II “ Corso di Ottimizzazione su Rete A.A. 2010/11. Outline of presentation. Context Flow intercepting facility location problems - PowerPoint PPT PresentationTRANSCRIPT
MODELS AND METHODS FOR THE OPTIMAL LOCATION OF
TRAFFIC SENSORS AND VMSs
A. SforzaDIS - Università di Napoli “Federico II “
Corso di Ottimizzazione su Rete
A.A. 2010/11
Outline of presentation
• Context– Flow intercepting facility location problems
• Applications in Traffic Management and Control
• Optimization models proposed in literature– Computational experience
– Proposals of new constraints
• A simple heuristic and some improving modifications
• Application to Traffic Network in Naples
Facility Location Problems
- Flow generating and/or attracting facilities
vertex – point – path
- Flow intercepting facilities
in vertices – on links
Flow generating and/or attracting facilitiesvertex location
Flow generating facilitiesService reaches the clients or vice-versa
Flow intercepting facilities
Flow intercepting facilities
Flow intercepting facilitiesO-D demand flows
Flow intercepting facilities in the vertices (two facilities)
Flow intercepting facilitieson the links (three facilities)
The flow intercepting facility
location problem
is a problem of path covering
Applications in Traffic Management and Control
• Location of:
– Traffic counting sensors (for o-d matrix estimation)• To know a set of link flows or all the link flows
– Variable message systems• Fixed• Mobile
– Traffic checkpoints
Applications – Service Facilities A classification scheme
• Voluntary service facilities– Car service stations, automatic teller machine
• Unconscious service facilities– Traffic counting sensors
• Unvoluntary service facilities– Variable message signs
• Compulsory service facilities– Traffic check points– Inspection Stations
Traffic Management and Control Applications
Traffic counting sensors
No need of double counting
Variable message systems
There could be the need of
double (or more) intercepting
m = 2 facilities
No double counting Double counting for path p2
p1 p2 p3 p1 p2 p3
Available information
- Information on path flows
- Information on link flows
- AssumptionThe flow pattern is not modified by facility location
This is surely true for traffic sensors
It could be not true for VMS
Information on path flows- Problem variablesInformation on path flows- Problem variables
j N
yj =10
if there is a facility located at node jotherwise,
p P
xp=if at least one of the facilities is located on path potherwise,
10
G = (N, A)
N, set of vertices; A, set of links p path, P set of paths
Max fp xp p P
n
s.t. yj = m j=1
yj xp pP j p
yj = 0, 1 xp = 0, 1
Model P1: Maximization of the intercepted flow with a fixed number of facilities
n
Min yj
j=1
s.t. yj xp pP (1)jp
fp xp C*.
(2)
pP
yj = 0, 1 jN
xp= 0, 1 pP
Model P2: Minimization of the facility number
to intercept a fixed % of the total demand
Intercepting all the demanded flows
pP fpxp C*
If we want to intercept all the demanded flows
that is if C* = pP fp
pP fpxp pP fp xp = 1 pP
The second constraint disappearsThe first set of constraints becames
j p yj 1
n
Min yj
j=1
s.t. yj 1 pP j p
yj = 0, 1 jN
Model P3: Minimization of the facility number to intercept the total demand
(i.e. to cover all the paths)
Model Output
Solving the model P1 produces the location of the m facilities giving the maximization of the intecepted flows, but it does not always give the exact values of the yp variables
Solving the model P2 produces the number and the location of the facilities needed to intercept a fixed percent of the total demand
and the list of the covered paths (i.e. exact values of yp variables)
Solving the model P3 produces the number and the location of the facilities needed to intercept the total demand (i.e. all the paths)
Location in vertices Location on links
Location in vertices is powerful for sensor location
to counting the flows of all the junction movement
It is possible from the technological viewpoint
using cameras and virtual sensors for each lane
and so for each movement in the junction.
Unfortunatly its result can be affected by errors,
sometimes relevant as we will see after.
For VMS location vertex location is not practicable,
because users have to be informed in the middle of the link
Transform a vertex model in a link modelthrough a dummy vertex
In any case a vertex model is much more manageable, because the number of variables is more tractable with respect to the number of variables of a link model.
Really it is possible to adopt a vertex model as a link model using a dummy vertex for each link
– For a single direction
– For both directions
Computational tests problem P1Nnodes_o/dpairs_pathsforodpairs_nodesforpaths
Network #nodes #plants Sol. value Gap% Time (secs)
N100_5_3_5 100 5 91 0.00 0.06
N200_10_3_10 200 10 183 0.00 0.05
N300_30_4_15 300 15 777 0.00 321.95
N500_50_5_20 500 25 1620 1.73 1h
N1000_100_5_25 1000 50 3020 5.40 1h
Computational tests problem P2 (60%)Nnodes_o/dpairs_pathsforodpairs_nodesforpaths
Network #nodes #plants Gap% Time (secs)
N100_5_3_5 100 2 0.00 0.02
N200_10_3_10 200 3 0.00 0.03
N300_30_4_15 300 7 0.00 0.78
N500_50_5_20 500 9 0.00 4.45
N1000_100_5_25 1000 16 0.00 593.22
Computational tests problem P3Nnodes_o/dpairs_pathsforodpairs_nodesforpaths
Network #nodes #plants Gap% Time (secs)
N100_5_3_5 100 3 0.00 0.02
N200_10_3_10 200 8 0.00 0.02
N300_30_4_15 300 17 0.00 134.56
N500_50_5_20 500 23 1.68 1h
N1000_100_5_25 1000 47 9.86 1h
Modification 1 of P2 model for traffic sensors location
The constraint (2) can be referred to a single o/d pair:
pPod fpxp C*
for each o/d pair of a given set of o/d pair
where Pod is the set of paths used to serve this o/d pair
Modification 2 of P2 model for traffic sensors and VMS location
To ensure that at least k paths of an od pair are intercepted the model can be integrated with the constraint:
pPod xp K
for each o/d pair of a given set of o/d pairwhere Pod is the set of paths used to serve this o/d pair
Modification 3 of P2 or P3 modelsfor VMS Location
To ensure that at least h plants intercept a path p
the model can be integrated with the constraint:
jp yj hfor each path p of a given set of relevant paths
Computational Times (sec)
Network: Modello P1 Modello P2(C) Modello P3
(C0) Number of nodes 60% 70% 80%
m t m t m t m t m t N100 5 0.23-1.92 3 1.70-0.81 4 2.59 (2) 5 2.43-3.63 8 0.89 N200 10 414.4-340.10 6 60.70-91.78 7 235.83-155.53 7-9 234.02-352.14 19 16.28 N300 8 20708.31 28 242.70 N500 38 1485.32 N700 49 2807.32
N1000 59 7916.89
CT Modification 1 of P2 Model
Network: Modello P2(C,k) Number of nodes 60% 70% 80%
m t m t m t N100 7 4.62 7 5.57 7 12.02 N200 16 1226.90 16 1546.22 16 5519.96 N300 N500 N700
N1000
CT Modification 2 of P2 Model
Network: Modello P2(C,P’) Number of nodes 60% 70% 80%
m t m t m t N100 18 329.51 18 312.38 * * N200
N300 N500 N700
N1000
Need of heuristic
For real networks with medium-large size
an heuristic approach seems unavoidable
A small network
1
2
765
4 3
Path 1: 1- 2 - 5 Path 2: 1 - 2 – 4 Path 3: 1 – 3 – 4 Path 4: 1 – 3 – 7
Path 5: 2 - 5 Path 6: 2 – 4 - 6 Path 7: 3 – 4 - 6 Path 8: 3 – 7
O/D paths
1
2
765
4 3
Path 1: 1- 2 – 5 (1) Path 2: 1 - 2 – 4 (2) Path 3: 1 – 3 – 4 (2) Path 4: 1 – 3 – 7(1)
Path 5: 2 – 5 (1) Path 6: 2 – 4 - 6 (1) Path 7: 3 – 4 – 6 (1) Path 8: 3 – 7 (1)
A greedy heuristic[Berman et al. (1992), Yang and Zhou (1998)]
Coverage matrix B (path/link incidence matrix)
The rows correspond to the paths p p P
The columns correspond to the links a a A
Each element bpa = 1 if link a belongs to the path p= 0 otherwise
The coverage matrix can be obtainedwith an assignment model
The coverage matrix B
Link
Path(flow) 1-2 1-3 2-4 3-4 2-5 4-6 3-7
p1(1) 1 0 0 0 1 0 0p2(2) 1 0 1 0 0 0 0p3(2) 0 1 0 1 0 0 0p4(1) 0 1 0 0 0 0 1p5(1) 0 0 0 0 1 0 0p6(1) 0 0 1 0 0 1 0p7(1) 0 0 0 1 0 1 0p8(1) 0 0 0 0 0 0 1
A greedy heuristic[Berman et al. (1992), Yang and Zhou (1998)]
Scheme of the heuristicStep 0: set k=0. Let B(k) be the coverage matrix
Step 1: Compute fa(k)= f a
(k) , a A
Step 2: Find aj: fJ (k)= max a A{ fa
(k) } and locate a facility in link aj
(if more than one choose the link with lowest index,or better,
choose the link belonging to the greatest number of paths)
Step 3: Update the coverage matrix and generate B(k+1)
deleting the column corresponding to link aj
(bpj(k+1)=0 p P)
deleting the rows corresponding to the paths intercepted from a j)
(bpa(k+1)=0 a A, for each p such that bpj
(k)=1
Step 4: if bpa=0 p P, a A , then STOP.
otherwise, set k=k+1 and return to step 1
First step of the heuristic
Link
Path(flow) 1-2 (3)
1-3 (3)
2-4 (3)
3-4 (3)
2-5 (2)
4-6 (2)
3-7 (2)
p1(1) 1 0 0 0 1 0 0p2(2) 1 0 1 0 0 0 0p3(2) 0 1 0 1 0 0 0p4(1) 0 1 0 0 0 0 1p5(1) 0 0 0 0 1 0 0p6(1) 0 0 1 0 0 1 0p7(1) 0 0 0 1 0 1 0p8(1) 0 0 0 0 0 0 1
Second step
Link
Path(flow) 1-2 (3)
1-3 (3)
2-4 (1)
3-4 (3)
2-5 (1)
4-6 (2)
3-7 (2)
p1(1) 1 0 0 0 1 0 0p2(2) 1 0 1 0 0 0 0p3(2) 0 1 0 1 0 0 0p4(1) 0 1 0 0 0 0 1p5(1) 0 0 0 0 1 0 0p6(1) 0 0 1 0 0 1 0p7(1) 0 0 0 1 0 1 0p8(1) 0 0 0 0 0 0 1
Third step
Link
Path(flow) 1-2 (3)
1-3 (3)
2-4 (1)
3-4 (1)
2-5 (1)
4-6 (2)
3-7 (1)
p1(1) 1 0 0 0 1 0 0p2(2) 1 0 1 0 0 0 0p3(2) 0 1 0 1 0 0 0p4(1) 0 1 0 0 0 0 1p5(1) 0 0 0 0 1 0 0p6(1) 0 0 1 0 0 1 0p7(1) 0 0 0 1 0 1 0p8(1) 0 0 0 0 0 0 1
Forth step
Link
Path(flow) 1-2 (3)
1-3 (3)
2-4 (0)
3-4 (0)
2-5 (1)
4-6 (2)
3-7 (1)
p1(1) 1 0 0 0 1 0 0p2(2) 1 0 1 0 0 0 0p3(2) 0 1 0 1 0 0 0p4(1) 0 1 0 0 0 0 1p5(1) 0 0 0 0 1 0 0p6(1) 0 0 1 0 0 1 0p7(1) 0 0 0 1 0 1 0p8(1) 0 0 0 0 0 0 1
Fifth and last step
Link
Path(flow) 1-2 (3)
1-3 (3)
2-4 (0)
3-4 (0)
2-5 (1)
4-6 (2)
3-7 (1)
p1(1) 1 0 0 0 1 0 0p2(2) 1 0 1 0 0 0 0p3(2) 0 1 0 1 0 0 0p4(1) 0 1 0 0 0 0 1p5(1) 0 0 0 0 1 0 0p6(1) 0 0 1 0 0 1 0p7(1) 0 0 0 1 0 1 0p8(1) 0 0 0 0 0 0 1
Comparison between heuristic and exact approach
This heuristic produces very fast solution,
but the result can be much far from the exact solution
Model P3 exact solution
4 facilities on links 2-4, 3-4, 2-5, 3-7
1
2
765
4 3
Path 1: 1- 2 – 5 (1) Path 2: 1 - 2 – 4 (2) Path 3: 1 – 3 – 4 (2) Path 4: 1 – 3 – 7(1)
Path 5: 2 – 5 (1) Path 6: 2 – 4 - 6 (1) Path 7: 3 – 4 – 6 (1) Path 8: 3 – 7 (1)
Greedy solution
5 facilities on links 1-2, 1-3, 2-5, 4-6, 3-7
1
2
765
4 3
Path 1: 1- 2 – 5 (1) Path 2: 1 - 2 – 4 (2) Path 3: 1 – 3 – 4 (2) Path 4: 1 – 3 – 7(1)
Path 5: 2 – 5 (1) Path 6: 2 – 4 - 6 (1) Path 7: 3 – 4 – 6 (1) Path 8: 3 – 7 (1)
A simple improvement of the heuristic
The heuristic can be improved in the step 2
Step 2: Find aj: fJ (k)= max a A{ fa
(k) } and locate a facility in link a
(if more than one choose the link with lowest index)
Alternative
1. Choose the link belonging to the greatest number of paths
2. Modify the selection criterion of the links
A simple network
3
2
41
5
10
O/D pair 1 – 9 2 – 9 2 – 10 3 – 10
Path 1: 1-4-7-9 Path 2: 2-5-7-9 Path 3: 2-5-8-10 Path 4: 3-6-8-10
6
7
8
9
Possible solution 1 (sub-optimal)
3
2
41
5
10
O/D pair 1 – 9 2 – 9 2 – 10 3 – 10
Path 1: 1-4-7-9 Path 2: 2-5-7-9 Path 3: 2-5-8-10 Path 4: 3-6-8-10
6
7
8
9
Possible solution 2 (optimal)
3
2
41
5
10
O/D pair 1 – 9 2 – 9 2 – 10 3 – 10
Path 1: 1-4-7-9 Path 2: 2-5-7-9 Path 3: 2-5-8-10 Path 4: 3-6-8-10
6
7
8
9
The coverage matrix B
1-4
(1)
2-5
(2)
3-6
(1)
4-7
(1)
5-7
(1)
5-8
(1)
6-8
(1)
7-9
(2)
8-10
(2)
p1 1 1 1
p2 1 1 1
p3 1 1 1
p4 1 1
Step 1a
1-4
(1)
2-5
(2)
3-6
(1)
4-7
(1)
5-7
(1)
5-8
(1)
6-8
(1)
7-9
(2)
8-10
(2)
p1 1 1 1
p2 1 1 1
p3 1 1 1
p4 1 1
Step 1b
1-4
(1)
2-5
(2)
3-6
(1)
4-7
(1)
5-7
(1)
5-8
(1)
6-8
(1)
7-9
(2)
8-10
(2)
p1 1 1 1
p2 1 1 1
p3 1 1 1
p4 1 1 1
Step 1b
1-4
(1)
2-5
(2)
3-6
(1)
4-7
(1)
5-7
(1)
5-8
(1)
6-8
(1)
7-9
(2)
8-10
(2)
p1 1 1 1
p2 1 1 1
p3 1 1 1
p4 1 1 1
Step1c
1-4
(1)
2-5
(0)
3-6
(1)
4-7
(1)
5-7(0+1)
5-8(0+1)
6-8
(1)
7-9(1+1)
8-10(1+1)
p1 1 1 1
p2 1 1 1
p3 1 1 1
p4 1 1 1
Step 2a
1-4
(1)
2-5
(0)
3-6
(1)
4-7
(1)
5-7(0+1)
5-8(0+1)
6-8
(1)
7-9(1+1)
8-10(1+1)
p1 1 1 1
p2 1 1 1
p3 1 1 1
p4 1 1 1
Step 2b
1-4(0+1)
2-5
(0)
3-6
(1)
4-7
(1)
5-7 (0+1)
5-8(0+1)
6-8
(1)
7-9(1+1)
8-10(1+1)
p1 1 1 1
p2 1 1 1
p3 1 1 1
p4 1 1 1
Step 2b
1-4(0+1)
2-5
(0)
3-6
(1)
4-7
(1)
5-7 (0+1)
5-8(0+1)
6-8
(1)
7-9(1+1)
8-10(1+1)
p1 1 1 1
p2 1 1 1
p3 1 1 1
p4 1 1 1
Step 2c
1-4(0+1)
2-5
(0)
3-6
(1)
4-7
(1)
5-7 (0+1)
5-8(0+1)
6-8
(1)
7-9
(0)
8-10(1+1)
p1 1 1 1
p2 1 1 1
p3 1 1 1
p4 1 1 1
Step 3a
1-4(0+1)
2-5
(0)
3-6
(1)
4-7
(0+1)
5-7 (0+1)
5-8(0+1)
6-8
(1)
7-9
(0)
8-10(1+1)
p1 1 1 1
p2 1 1 1
p3 1 1 1
p4 1 1 1
Step 3b
1-4(0+1)
2-5
(0)
3-6
(1)
4-7
(0+1)
5-7 (0+1)
5-8(0+1)
6-8
(1)
7-9
(0)
8-10(1+1)
p1 1 1 1
p2 1 1 1
p3 1 1 1
p4 1 1 1
Step 3b
1-4(0+1)
2-5
(0)
3-6
(1)
4-7
(0+1)
5-7 (0+1)
5-8(0+1)
6-8
(1)
7-9
(0)
8-10(1+1)
p1 1 1 1
p2 1 1 1
p3 1 1 1
p4 1 1 1
Step 3c (all the flows are intercepted)
1-4(0+1)
2-5
(0)
3-6(0+1)
4-7
(0+1)
5-7 (0+1)
5-8(0+1)
6-8(0+1)
7-9
(0)
8-10
(0)
p1 1 1 1
p2 1 1 1
p3 1 1 1
p4 1 1 1
Check of the solution links
1-4(0+1)
2-5
(0)
3-6(0+1)
4-7
(0+1)
5-7 (0+1)
5-8(0+1)
6-8(0+1)
7-9
(0)
8-10
(0)
p1 1 1 1
p2 1 1 1
p3 1 1 1
p4 1 1 1
Final Solution
1-4(0+1)
2-5
(0)
3-6(0+1)
4-7
(0+1)
5-7 (0+1)
5-8(0+1)
6-8(0+1)
7-9
(0)
8-10
(0)
p1 1 1 1
p2 1 1 1
p3 1 1 1
p4 1 1 1
Revised greedy heuristic
Step 0: set k=0. Let B(k) be the coverage matrix
Step 1: For each link a A, compute [f1a(k), f2a
(k) ]
Where: f1a(k) is the flow to intercept
f2a(k) is the flow already intercepted
Step 2: Sort the links in decreasing lexicografic order with respect to the couple [f1a
(k), f2a(k) ] and locate a facility in the first link aj
Step 3: Update the coverage matrix and generate B(k+1)
- deleting the column corresponding to link aj
(bpj(k+1)=0 p P)
-deleting the rows corresponding to the paths intercepted with link aj
(bpa(k+1)=0 a A, for each p such that bpj
(k)=1)
Step 4: if bpa=0 p P, a A , then GoTo the Step 5. otherwise, set k=k+1 and return to Step 1
Step 5: Check the links inserted in the solution:If a link intercept flows intercepted from other links,remove it from the solution.
A modification of the heuristic
The heuristic can be adapted
to the VMS location problem
when it is necessary
to intercept twice or more a specific path
I step of the modified heuristic
Link
Path(flow) 1-2 (3)
1-3 (3)
2-4 (3)
3-4 (3)
2-5 (2)
4-6 (2)
3-7 (2)
p1(1) 1 0 0 0 1 0 0p2(2) 1 0 1 0 0 0 0p3(2) 0 1 0 1 0 0 0p4(1) 0 1 0 0 0 0 1p5(1) 0 0 0 0 1 0 0p6(1) 0 0 1 0 0 1 0p7(1) 0 0 0 1 0 1 0p8(1) 0 0 0 0 0 0 1
L
1 1
1 2
0 2
0 1
0 1
0 1
0 1
0 1
II step of the modified heuristic
Link
Path(flow) 1-2 (0)
1-3 (3)
2-4 (3)
3-4 (3)
2-5 (1)
4-6 (2)
3-7 (2)
p1(1) 1 0 0 0 1 0 0p2(2) 1 0 1 0 0 0 0p3(2) 0 1 0 1 0 0 0p4(1) 0 1 0 0 0 0 1p5(1) 0 0 0 0 1 0 0p6(1) 0 0 1 0 0 1 0p7(1) 0 0 0 1 0 1 0p8(1) 0 0 0 0 0 0 1
L
1 1
1 2
1 2
1 1
0 1
0 1
0 1
0 1
III step of the modified heuristic
Link
Path(flow) 1-2 (0)
1-3 (0)
2-4 (3)
3-4 (3)
2-5 (1)
4-6 (2)
3-7 (1)
p1(1) 1 0 0 0 1 0 0p2(2) 1 0 1 0 0 0 0p3(2) 0 1 0 1 0 0 0p4(1) 0 1 0 0 0 0 1p5(1) 0 0 0 0 1 0 0p6(1) 0 0 1 0 0 1 0p7(1) 0 0 0 1 0 1 0p8(1) 0 0 0 0 0 0 1
L
1 1
2 2
1 2
1 1
0 1
1 1
0 1
0 1
IV step of the modified heuristic
Link
Path(flow) 1-2 (0)
1-3 (0)
2-4 (0)
3-4 (3)
2-5 (1)
4-6 (1)
3-7 (1)
p1(1) 1 0 0 0 1 0 0p2(2) 1 0 1 0 0 0 0p3(2) 0 1 0 1 0 0 0p4(1) 0 1 0 0 0 0 1p5(1) 0 0 0 0 1 0 0p6(1) 0 0 1 0 0 1 0p7(1) 0 0 0 1 0 1 0p8(1) 0 0 0 0 0 0 1
L
1 1
2 2
2 2
1 1
0 1
1 1
1 1
0 1
V step of the modified heuristic
Link
Path(flow) 1-2 (0)
1-3 (0)
2-4 (0)
3-4 (0)
2-5 (1)
4-6 (0)
3-7 (1)
p1(1) 1 0 0 0 1 0 0p2(2) 1 0 1 0 0 0 0p3(2) 0 1 0 1 0 0 0p4(1) 0 1 0 0 0 0 1p5(1) 0 0 0 0 1 0 0p6(1) 0 0 1 0 0 1 0p7(1) 0 0 0 1 0 1 0p8(1) 0 0 0 0 0 0 1
L
1 1
2 2
2 2
1 1
1 1
1 1
1 1
0 1
VI step of the modified heuristic
Link
Path(flow) 1-2 (0)
1-3 (0)
2-4 (0)
3-4 (0)
2-5 (0)
4-6 (0)
3-7 (1)
p1(1) 1 0 0 0 1 0 0p2(2) 1 0 1 0 0 0 0p3(2) 0 1 0 1 0 0 0p4(1) 0 1 0 0 0 0 1p5(1) 0 0 0 0 1 0 0p6(1) 0 0 1 0 0 1 0p7(1) 0 0 0 1 0 1 0p8(1) 0 0 0 0 0 0 1
L
1 1
2 2
2 2
1 1
1 1
1 1
1 1
1 1
VII and last step of the modified heuristic
Link
Path(flow) 1-2 (0)
1-3 (0)
2-4 (0)
3-4 (0)
2-5 (0)
4-6 (0)
3-7 (0)
p1(1) 1 0 0 0 1 0 0p2(2) 1 0 1 0 0 0 0p3(2) 0 1 0 1 0 0 0p4(1) 0 1 0 0 0 0 1p5(1) 0 0 0 0 1 0 0p6(1) 0 0 1 0 0 1 0p7(1) 0 0 0 1 0 1 0p8(1) 0 0 0 0 0 0 1
L
1 1
2 2
2 2
1 1
1 1
1 1
1 1
1 1
Greedy solution
6 facilities on links 1-2, 1-3, 2-4, 3-4, 2-5, 3-7
1
2
765
4 3
Path 1: 1- 2 – 5 (1) Path 2: 1 - 2 – 4 (2) Path 3: 1 – 3 – 4 (2) Path 4: 1 – 3 – 7(1)
Path 5: 2 – 5 (1) Path 6: 2 – 4 - 6 (1) Path 7: 3 – 4 – 6 (1) Path 8: 3 – 7 (1)
Applications to TM in Naples
ATENA Project (1999-2002)(MURST, City of Naples, FIAT, University of Naples)
Low emission vehicle fleet experimentation
Telematic system for traffic management
Traffic monitoring and VMS
Traffic Supervisor
Work perspectives
Methodological schemeSensor location
UTM
VMS location
Process Scheme in ATIS scenario
Flow monitoring
Traffic Management
Message to the users
User behaviour and modification of the flow pattern
Return to Flow monitoring and Iterate
Joint research perspectives
Proposal of research project (Prin 2003):
Infomobility and Transportation Network DesignRoma “La Sapienza” (coordination)
Camerino
Genova
Milano Politecnico
Napoli “Federico II”
References
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