Impact of Data Analytics-Driven Signal Timing on Transit Travel Times and Pedestrian Safety in Urban Corridors

Data analytics-driven signal timing interventions, including Transit Signal Priority (TSP), adaptive traffic control, machine learning-based predictive timing, and multi-component strategies, demonstrably enhance public transit efficiency in high-density urban corridors while yielding mixed pedestrian safety outcomes. A semantic search retrieved relevant studies, with screened in based on urban settings, data-driven methods, quantitative before-after designs, real-world implementations, and routine operations focus. Predominantly before-after evaluations and Bayesian comparisons, the interventions primarily TSP and adaptive controls—reduced transit travel times by ۱۷.۲% (e.g., corridor from ۶۰ to ۴۰ minutes; typical ~۱۱%), halved intersection delays (۰.۱%), and cut bus delays (e.g., BRT from ۲۰ to ۱۰ seconds). Reliability improved in four studies through smoother operations and better on-time performance, with adaptive systems resilient to ۵۰% traffic growth. Pedestrian safety was inconsistently addressed: one interrupted time series noted a ۱۵% crash increase post-TSP (CMF ۱.۷), signaling potential trade-offs from exposure or inadequate phasing. Conversely, ML and adaptive approaches boosted mobility, reducing wait times with ۹۵% prediction accuracy and ۱% delay estimation error. Seven studies omitted safety data, underscoring evaluation gaps. These findings highlight context-dependent effectiveness, urging pedestrian-inclusive analytics for equitable multimodal corridors. Future research should emphasize conflict analyses and longitudinal assessments to balance efficiency and safety.

Keywords:

Transit Signal Priority (TSP) , Adaptive traffic control , Machine learning-based timing , Travel time reductions , Service reliability