|M.Sc Student||Liat Mashiach|
|Subject||Statistical-Based Method for Parking Search on the Way|
|Department||Department of Civil and Environmental Engineering||Supervisors||Full Professor Greenfeld Joshua S.|
|Dr. Dalyot Sagi|
|Full Thesis text|
Driving to a place where we don’t know where to park, for part of us it makes nervous or anxious feelings. Moreover, when we reach our destination and start searching for a parking place, the nervous and anxious feelings are exacerbating corresponding to increasing of cruising time. Sometimes, drivers can give up their planned journey or chose to reach another place so they won't experience this situation.
According to Shoup,2006 the average parking-search time is evaluated to be eight minutes on average in several major cities. He also shows from different studies results that 8 to 74 percent of the traffic was for parking cruising, and the average time to find a curb space ranged between 3.5 to 14 min. By Shoup, 2006, the wide variance in the estimates of cruising surely reflects reality. On most streets most of the time, none of the traffic is cruising, but on some streets some of the time, most of the traffic may be cruising.
Even though there are a lot of parking lots, drivers prefer to cruise for on street parking since it is the cheapest parking option. Hence, on-street parking spots are much in demand and therefore are difficult to find.
This research tries to reduce cruising time for parking by suggesting a model of software system for parking using search algorithm. The model use statistic spatial-temporal park information to choose the best parking search routes in terms of high probability of parking success, proximity to the destination and minimum overall searching time.
In general, our model builds parking search routes from origin to destination based on minimum parking expectation time. Parking expectation time indicates the expected average time spent until finding an available parking space. The model calculates this expectation by using statistical parking data that holds parking capacity information of the area the search is conducted. The model chooses the best parking search routes by selecting the routes with the minimum results of the following calculations:
• Parking expectation time
• Walking expectation time to destination
• Traveling expectation time
Experimentally, we observed that model success to find efficient routes with greater parking opportunities, where most of them located in the vicinity of their destination.