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• 1. [Article] Value of Travel Time Reliability Part II: A Study of Tradeoffs Between Travel Reliability, Congestion Mitigation Strategies and Emissions

  Capacity, demand, and vehicle based emissions reduction strategies are compared for several pollutants employing aggregate US congestion and vehicle fleet condition data. We find that congestion mitigation ...

  Citation × Citation Citation Abstract Copy Title: Value of Travel Time Reliability Part II: A Study of Tradeoffs Between Travel Reliability, Congestion Mitigation Strategies and Emissions Author: Figliozzi, Miguel A., Bigazzi, Alexander Y. Year: 2012 Capacity, demand, and vehicle based emissions reduction strategies are compared for several pollutants employing aggregate US congestion and vehicle fleet condition data. We find that congestion mitigation does not inevitably lead to reduced emissions; the net effect of mitigation depends on the balance of induced travel demand and increased vehicle efficiency that in turn depend on the pollutant, congestion level, and fleet composition. In the long run, capacity-based congestion improvements within certain speed intervals can reasonably be expected to increase emissions of CO2e, CO, and NOx through increased vehicle travel volume. Better opportunities for emissions reductions exist for HC and PM2.5 emissions, and on more heavily congested arterials. Advanced-efficiency vehicles with emissions rates that are less sensitive to congestion than conventional vehicles generate less emissions co-benefits from congestion mitigation. Title: Value of Travel Time Reliability Part II: A Study of Tradeoffs Between Travel Reliability, Congestion Mitigation Strategies and Emissions Author: Figliozzi, Miguel A., Bigazzi, Alexander Y. Year: 2012 Capacity, demand, and vehicle based emissions reduction strategies are compared for several pollutants employing aggregate US congestion and vehicle fleet condition data. We find that congestion mitigation does not inevitably lead to reduced emissions; the net effect of mitigation depends on the balance of induced travel demand and increased vehicle efficiency that in turn depend on the pollutant, congestion level, and fleet composition. In the long run, capacity-based congestion improvements within certain speed intervals can reasonably be expected to increase emissions of CO2e, CO, and NOx through increased vehicle travel volume. Better opportunities for emissions reductions exist for HC and PM2.5 emissions, and on more heavily congested arterials. Advanced-efficiency vehicles with emissions rates that are less sensitive to congestion than conventional vehicles generate less emissions co-benefits from congestion mitigation. Close

• 2. [Article] Factors Influencing Effectiveness of Transit Signal Priority and Late-Bus Recovery at Signalized-Intersection Level

  Transit priority systems have the potential to improve transit performance and address capacity constraints by giving priority to transit movements over other traffic. This research focused on the effectiveness ...

  Citation × Citation Citation Abstract Copy Title: Factors Influencing Effectiveness of Transit Signal Priority and Late-Bus Recovery at Signalized-Intersection Level Author: Albright, Eric, Figliozzi, Miguel A. Year: 2012 Transit priority systems have the potential to improve transit performance and address capacity constraints by giving priority to transit movements over other traffic. This research focused on the effectiveness of conditional transit priority or the manipulation of traffic signal timing plans to reduce delay of late transit buses. The integration of two transportation subsystems—traffic signals and public transit systems—was studied. These subsystems interact along a congested corridor where they share a common roadway infrastructure and transit signal priority (TSP) regulates the interaction between traffic signals, passenger traffic, and buses. Previous research has focused on the evaluation of bus TSP performance at the route level. In practice, it is important to understand not only TSP performance at the route level but also the impact of TSP at the level of the traffic signal intersection (e.g., to allow progression in major cross streets). TSP can significantly improve performance at specific intersections, even though at the route level TSP shows a more modest impact. This study proposed the integration of several data sets such as bus scheduling and location, passenger flows, and TSP requests to evaluate schedule adherence at the stop level and TSP performance at the level of the signalized intersection. A congested arterial corridor was analyzed and regression analysis was used to determine the key factors that affect bus travel time and schedule recovery for late buses. TSP was found to be most effective at lower-volume intersections where queuing was less problematic. Implications of the findings are analyzed and discussed. Title: Factors Influencing Effectiveness of Transit Signal Priority and Late-Bus Recovery at Signalized-Intersection Level Author: Albright, Eric, Figliozzi, Miguel A. Year: 2012 Transit priority systems have the potential to improve transit performance and address capacity constraints by giving priority to transit movements over other traffic. This research focused on the effectiveness of conditional transit priority or the manipulation of traffic signal timing plans to reduce delay of late transit buses. The integration of two transportation subsystems—traffic signals and public transit systems—was studied. These subsystems interact along a congested corridor where they share a common roadway infrastructure and transit signal priority (TSP) regulates the interaction between traffic signals, passenger traffic, and buses. Previous research has focused on the evaluation of bus TSP performance at the route level. In practice, it is important to understand not only TSP performance at the route level but also the impact of TSP at the level of the traffic signal intersection (e.g., to allow progression in major cross streets). TSP can significantly improve performance at specific intersections, even though at the route level TSP shows a more modest impact. This study proposed the integration of several data sets such as bus scheduling and location, passenger flows, and TSP requests to evaluate schedule adherence at the stop level and TSP performance at the level of the signalized intersection. A congested arterial corridor was analyzed and regression analysis was used to determine the key factors that affect bus travel time and schedule recovery for late buses. TSP was found to be most effective at lower-volume intersections where queuing was less problematic. Implications of the findings are analyzed and discussed. Close