Hamid Afshari

Decarbonizing urban bus fleets requires not only replacing conventional vehicles with cleanertechnologies but also attracting indifferent passengers (i.e., gray passengers) who may choose private cars or ride-sourcing services. This study develops a bi-level framework to analyze behavior of gray passengers in the bus fleet transition problem under competing transportation modes. In the proposed framework, the public authority sets a policy portfolio while gray passengers adjust their modal choices, and the transit operator decides on the share of green fleet. The framework embraces equity considerations by capturing key determinants of passenger choice in mode selection. The results indicate that the optimized policy shifts approximately 59.6% of gray passengers to sustainable urban buses, while 23.9% remain with private cars and 16.5% choose ride-sourcing services. The findings suggest that an effective transit decarbonization plan requires an integrated policy design that jointly considers fleet transition, competing mobility services, and adaptive passenger behavior.

Decarbonizing urban bus fleets requires not only replacing conventional vehicles with cleanertechnologies but also attracting indifferent passengers (i.e., gray passengers) who may otherwise choose private cars or ride-sourcing services. This study develops a bi-level framework, using multiobjective optimization and evolutionary game, to analyze the behaviour of gray passengers in the bus fleet transition problem under competing transportation modes. The leader (public authority) selects a policy portfolio (i.e., green fleet support subsidies, non-green fleet penalties, ride-sourcing levies, congestion charges, and carbon pricing policy). In response, gray passengers adjust their modal choices among bus, private car, and ride-sourcing, while the transit operator adjusts the green share of its fleet through lower-level evolutionary preference dynamics. The framework embraces equity considerations by capturing key determinants of passenger choice in mode selection (i.e.,travel time, service frequency, crowding, fare, and ride-comfort attributes such as noise, vibration, thermal comfort, and acceleration harshness). The results show that the optimized policy shifts approximately 59.6% of gray passengers to sustainable urban buses, while 23.9% remain with private cars and 16.5% choose ride-sourcing services. The route-level results reveal substantial heterogeneity, indicating that system-wide policy levies should be complemented by route-sensitive service planning.Sensitivity analyses show that gray-market size primarily affects the magnitude of induced bus demand; congestion charges and ride-sourcing levies should be coordinated to avoid cross-substitution; and green-fleet adoption follows a clear subsidy–cost threshold. The findings suggest that effective transit decarbonization requires an integrated policy design that jointly considers fleet transition, competing mobility services, and adaptive passenger behavior.

Retailing plays a pivotal role in the functioning of urban systems. While upstream supply chain activities such as manufacturing and distribution primarily affect freight movement, the retail interface translates consumer demand into individual travel behavior, shaping local traffic conditions and feeding back into upstream logistics. Despite its importance, shopping-related travel remains under-modeled in urban mobility research. To address this gap, this study develops a purpose-specific travel forecasting and simulation framework for predicting shopping trip demand in urban areas. The forecasting model integrates commercial-environment attributes, trip characteristics, and sociodemographic factors. A suite of machine learning (ML) models is evaluated, and the best-performing model is selected for the proposed simulation. Microlevel predictions are then scaled to the full urban region, followed by zonal aggregation and k -means spatial clustering to identify distinct retail-demand patterns and support scenario testing. Numerical results show that the random forest model outperforms alternative ML classifiers and, when implemented in the simulation, generates a citywide estimate indicating that shopping trips represent 14.3% of all weekday travel, in line with external regional benchmarks. The combined ML–simulation framework demonstrates strong predictive performance and reveals meaningful spatial and behavioral insights relevant to policymaking and planning applications. Although applied to Halifax, the modular structure of the framework makes it transferable to other urban regions and adaptable to additional trip purposes, supporting future extensions involving multiactivity modeling, causal impact analysis, and integration with passive mobility datasets.