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Contextual features are important data sources for building citywide crowdmobility prediction models. However, the difficulty of applying context lies inthe unknown generalizability of contextual features (e.g., weather, holiday,and points of interests) and context modeling techniques across differentscenarios. In this paper, we present a unified analytic framework and alarge-scale benchmark for evaluating context generalizability. The benchmarkincludes crowd mobility data, contextual data, and advanced prediction models.We conduct comprehensive experiments in several crowd mobility prediction taskssuch as bike flow, metro passenger flow, and electric vehicle charging demand.Our results reveal several important observations: (1) Using more contextualfeatures may not always result in better prediction with existing contextmodeling techniques; in particular, the combination of holiday and temporalposition can provide more generalizable beneficial information than othercontextual feature combinations. (2) In context modeling techniques, using agated unit to incorporate raw contextual features into the deep predictionmodel has good generalizability. Besides, we offer several suggestions aboutincorporating contextual factors for building crowd mobility predictionapplications. From our findings, we call for future research efforts devoted todeveloping new context modeling solutions.

Exploring Context Generalizability in Citywide Crowd Mobility Prediction: An Analytic Framework and Benchmark