Patrol Routing Expression, Execution, Evaluation, and Engagement

Landscapes of Risk: The Geography of Fatal Traffic Collisions in Indiana, 2003 to 2011

“…Understanding spatial risk patterns can help inform strategies, programs, and preventative tactics to respond more effectively and quickly to crashes, prevent future collisions, and reduce mortality rates in targeted areas. Knowing spatial patterns and the types of spatial dependence among counties can help tailor better preparedness and response to situations most likely to be encountered (Kuo, Lord, and Walden 2011;Steil et al 2011). Different enforcement strategies could be envisioned in response to dispersed or clustered patterns.…”

Multiobjective Dynamic Vehicle Routing Problem and Time Seed Based Solution Using Particle Swarm Optimization

“…(2) for = 1 to sr // Generating CRV (4) generate CRV ( , PLDT, PUDT, STIL, STIU, ECPUT) randomly (5) endfor (6) for = 1 to sr // Generating OV (7) generate GR vector (ADR, Dist., RN, SR) randomly and calculate Gr using (1) (8) generate CRK vector (VIC, IC, RC, CC) randomly and assign weight according to ranks (9) calculate ST using (2) (10) calculate ERT using (3) (11) endfor (12) Partition ( , , , V ) into sub-networks as = ⋃ =1 (13) for = 1 to (14) Divide time horizon into time seeds as = 1, + 2, + 3, ⋅ ⋅ ⋅ + , (15) for each time seed , (16) dr = rand(0 − ) (17) for = 1 to dr // Generating Customer Request Vector for Dynamic Requests (18) generate CRV ( , PLDT, PUDT, STIL, STIU, ECPUT) randomly (19) endfor (20) for = 1 to dr // Generating Customer Order Vector for Dynamic Requests (21) generate GR vector (ADR, Dist., RN, SR) randomly and calculate Gr using (1) (22) generate CRK vector (VIC, IC, RC, CC) and assign weight according to ranks (23) calculate ST using (2) (24) calculate ERT using (3) (25) endfor (26) = 0 (27) Generate position ( ) and velocity ( ) for th particle in th generation from COV (28) while (| best ( ) − best ( − 1) < |) do (29) = + 1 (30) for each particle ( ( ), ( )) of the search space (31) evaluate fitness using objective function (5) best ( ) = ( ) (34) endfor (35) best ( ) = best 1 ( ) (36) for = 2 to number of particles in the swarm (37) if (Fitt[ ( best ( ), ( ))] > Fitt[ ( best ( ), ( ))]) (38) best ( ) = best ( ) (39) endfor (40) endwhile (41) store best ( ) for th time seed (42) endfor (43) store the set of best ( ) for th partition (44) endfor Algorithm 1: TS-PSO.…”

“…Travel distance and waiting time for vehicles have been minimized using heuristic cost sharing methods. A patrol routing algorithm has been constructed in [16] for police, ambulance, and taxi services. The algorithm has been explored in terms of expression, execution, evaluation, and engagement.…”

Athos - A Model Driven Approach to Describe and Solve Optimisation Problems

“…In [15] Steil et al present a model for the the expression, execution, evaluation and engagement of routing plans to which they refer as the 4Es model. They use the model to map all relevant steps in the domain of patrol routing to appropriate software components.…”