Estimating Parking Time Under Batch Arrival and Dynamic Pricing Policy

As the urban population and the car ownership rate increase, traffic is becoming a serious urban problem. It remains difficult to reduce the large number of vehicles circling in search for free and cheaper lots in urban area. The driver's choice for on-street lots, which are generally cheaper than off-street lots but also rare, causes congestion, impact negatively the parking revenues and increases the total cost of drivers' trips. However, we propose to intervene on price as effective tool to influence driver's behavior, balance parking demand and enhance parking turnover in urban environment. This article highlights the interactions between parking price, real-time parking demand and parking time. We analyze the parking time under near-real-life conditions using the Discrete Batch Markovian Arrival Process (D-BMAP). Subsequently, we identify the optimal arrival rate that will seek to make the best use of parking resources. Finally, we propose to adopt a dynamic pricing policy that changes prices proportionally to the arrival rates on each parking and therefore reduces congestion (cruising time) and eliminates the driver's preference for some parking.