Restaurant Density and Delivery Speed in Food Delivery Platforms

Food delivery platforms (DoorDash, Uber Eats, Meituan) are racing to expand by signing as many restaurants as possible. Such expansion directly translates into changes in restaurant density on the platform, making it crucial to understand how density affects restaurant outcomes. This paper examines the impact of restaurant density on incumbent restaurants and compares three key externalities induced by increased density: customer expansion and cannibalization, which are well documented in the literature, and delivery pooling (i.e., drivers being closer to restaurants and bundling multiple orders before delivery), which has received much less attention. Using a queueing-theoretical model, we develop testable hypotheses on how these externalities influence delivery speed, sales, revenues, and customer spending. Employing two-way fixed effects and instrumental variable estimation with data from a major Chinese food delivery platform, we find that higher restaurant density improves delivery speed and sales for partnered restaurants but reduces order size; heterogeneous-effects estimation further reveals that fast-food and high-volume restaurants benefit more from increased density, while made-to-order and thermally sensitive cuisines see weaker gains. Combining the empirical evidence with our theoretical predictions, our evidence is most consistent with delivery pooling playing a more pivotal role than customer expansion or cannibalization in shaping density effects. Furthermore, nonlinear least squares estimation of our theoretical model reveals that driver arrival speed at restaurants rises with density, shortening customers' wait times and inducing them to place more—but smaller—orders. However, this effect follows an inverted-U-shaped pattern, indicating diminishing delivery pooling benefits—and even adverse effects—at very high density. Finally, we extend our theoretical framework to develop a simulation model, estimated using data, which allows us to isolate the effect of each externality and identify restaurant-specific optimal densities through counterfactual simulations. Results show substantial heterogeneity in sales-maximizing densities across restaurants, with most operating below their optima. Our findings also suggest that platforms should prioritize expansion in low- to moderate-density regions to maximize delivery efficiency and restaurant sales, while carefully managing congestion and tailoring support based on restaurant type in denser markets.