Neighborhood-Specific Traffic Impact Analysis Of Restaurant Meal Delivery Trips: Planning Implications And Case Studies In Chicago

JOURNAL OF URBAN PLANNING AND DEVELOPMENT(2021)

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摘要
This study explores how strategic planning may help avoid unnecessary increases in the transportation impacts of growing urban delivery services. Restaurant meal delivery (RMD) is studied as an example to demonstrate how the preferred method and local impacts of delivery vehicle trips may vary by neighborhood characteristics (e.g., restaurant density and service areas, customer demand, and delivery driver availability). Instead of seeking route optimization solutions, this study developed a generic model to estimate the vehicle miles traveled (VMT) per meal order for three generalized types of neighborhoods and six delivery scenarios that involve one or more strategies: (1) chaining customers; (2) chaining restaurants; (3) customers picking up orders at a designated location (instead of door-to-door services); and (4) crowdsourcing drivers. The results from the model implementation in Chicago showed that alternative delivery strategies can reduce the VMT per order from one-to-one RMD by 16.1% to 61.1%, and the reduction effects of these strategies are varied by neighborhood. Great reductions in VMT per order could be achieved when the delivery time was extended (e.g., from 45 to up to 90 min). Accordingly, this study recommended neighborhood-specific policies for delivery services (e.g., chaining orders in areas with clustered restaurants offering small service zones and crowdsourcing drivers in areas with clustered restaurants offering large service zones), incentivizing socially preferable strategies (e.g., subsidizing customers willing to accommodate longer delivery time), and the public sector's role in coordinating among service providers for neighborhood-conscious services.
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关键词
Urban delivery services, Sustainability impacts, Restaurant meal delivery, Continuous approximation, Urban transportation policy, Demand-responsive models
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