چکیده مقاله
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 17 2% e g , corridor from 60 to 40 minutes; typical ~11% , halved intersection delays 0 1% , and cut bus delays e g , BRT from 20 to 10 seconds Reliability improved in four studies through smoother operations and better on time performance, with adaptive systems resilient to 50% traffic growth Pedestrian safety was inconsistently addressed: one interrupted time series noted a 15% crash increase post TSP CMF 1 7 , signaling potential trade offs from exposure or inadequate phasing Conversely, ML and adaptive approaches boosted mobility, reducing wait times with 95% prediction accuracy and 1% 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
کلیدواژهها
نویسندگان
شیوه ارجاع
Rezvani, Bahram,1404,Impact of Data Analytics-Driven Signal Timing on Transit Travel Times and Pedestrian Safety in Urban Corridors,the 14th International Conference on Strategic Ideas in Architecture, Civil Engineering and Urban Planning in Iran,Mashhad
ارائهشده در
مجموعه مقالات چهاردهمین کنفرانس بین المللی ایده های راهبردی در معماری، عمران و شهرسازی ایران26 آبان 1404 · مشهد