Data stories from urban loading bays

Freight vehicle parking facilities at large urban freight traffic generators, such as urban retail malls, are often characterized by a high volume of vehicle arrivals and a poor parking supply infrastructure. Recurrent congestion of freight parking facilities generates negative externalities.

Solutions aimed at either improving or better managing the existing parking infrastructure rely heavily on data and data-driven models to predict their impact and guide their implementation. In their paper the researchers provide a quantitative study of the parking supply and freight vehicle drivers’ parking behaviour at urban retail malls.

Unloading data

The researchers used as case studies two typical urban retail malls located in Singapore, and collect detailed data on freight vehicles delivering or picking up goods at these malls. Insights from this data collection effort are relayed as data stories. They first describe the parking facility at a mall as a queueing system, where freight vehicles are the agents and their decisions are the parking location choice and the parking duration.

Using the data collected, the researchers analysed:

  • the arrival rates of vehicles at the observed malls
  • the empirical distribution of parking durations at the loading bays,
  • the factors that influence the parking duration
  • the empirical distribution of waiting times spent by freight vehicle queueing to access the loading bay
  • the driver parking location choices and how this choice is influenced by system congestion.

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This characterisation of freight driver behaviour and parking facility system performance enables companies and local government to understand current challenges, and begin to explore the feasibility of freight parking and loading bay management solutions.

Read the full paper here.

Data stories from urban loading bays
Dalla Chiara G, Cheah L in: Eur. Transp. Res. Rev. (2017) 9: 50.

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