Nov 2026· Journal of Transportation Engineering Part A Systems· Vol 152· 0 citations· 42 references
Abstract
Effective pedestrian traffic management at intersections is vital for ensuring safety and operational efficiency in urban transportation systems. Due to the global consensus that vehicles must yield to pedestrians at intersections, turning vehicles often experience long queues, which even lead to intersection congestion, particularly under high pedestrian traffic demand. However, existing signal strategies, including two-way crossings (TWCs), leading pedestrian intervals (LPIs), and exclusive pedestrian phases (EPPs), do not effectively resolve such traffic issue. Thus, this study proposes a nested pedestrian phase (NPP) control strategy that periodically suspends pedestrian crossing through short red intervals embedded within the pedestrian green phase, enabling queued turning vehicles to clear in stages. An optimization model is formulated to minimize total cost comprising safety and efficiency components quantified by vehicle–pedestrian conflict frequency and delay, yielding optimal configurations of nested red intervals under varying demand patterns. The model is validated through a simulation model built through VISSIM based on a signalized intersection in Shanghai, China. Validation results demonstrate that the proposed model achieves well-balanced performance with a decrease in vehicle–pedestrian conflict frequency by 52.8% as compared to TWC, with the pedestrian delay increasing by only 3.4%. Sensitivity analyses further reveal that the NPP strategy improves vehicle efficiency by an average of 13.6% across various traffic demand conditions, with pedestrian crossing efficiency reduced by no more than 1.08%. Overall, NPP effectively improves traffic efficiency of both vehicles and pedestrians at intersections by significantly mitigating vehicle–pedestrian conflicts, with only a modest trade-off in pedestrian mobility and equity.
Adaptive signal control is developed that embeds pedestrian conflict risk directly in the optimization objective rather than through heuristic constraints or phase restrictions and achieves competitive travel times and a more favorable efficiency–safety trade-off than fixed-time control.
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