Dual Influences on Vehicle Speeds in Special-Use Lanes and Policy Implications

Slow speeds in a special-use lane, such as a carpool (HOV) or bus lane, can be due to both high demand for that lane and slow speeds in the adjacent regular-use lane. These dual influences are confirmed from months of data collected from all freeway carpool facilities in the San Francisco Bay Area. Both influences hold for other types of special-use lanes, including bus lanes. New US regulation stipulating that most classes of low-emitting vehicles, or LEVs, be banned from slow-moving carpool lanes. While LEVs invariably constitute only about 1 percent of the freeway traffic demand in the San Francisco Bay Area, forcing some or all of these vehicles to regular-use lanes can significantly add to regular-lane congestion, and that this, in turn, can also be damaging to vehicles that continue to use the carpool lanes. Counterproductive outcomes of this kind are predicted first by applying kinematic wave analysis to a real Bay Area freeway. The site stands to suffer less from the regulation than will others in the region but the site’s people-hours and vehicle-hours traveled during the rush are predicted to each increase by more than 10 percent and that carpool-lane traffic will share in the damages. Real data from the site support these predictions. Further parametric analysis of a hypothetical, but more generic freeway system indicates that these kinds of negative outcomes will be widespread. Constructive ways to amend the new regulation are discussed, as are promising strategies to increase the vehicle speeds in carpool lanes by improving the travel conditions in regular lanes.

[1]  Michael J. Cassidy,et al.  Dual influences on vehicle speed in special-use lanes and critique of US regulation , 2012 .

[2]  Kitae Jang,et al.  A Dynamic Congestion Pricing Strategy for High-Occupancy Toll Lanes , 2010 .

[3]  Michael J. Cassidy,et al.  Relation Among Average Speed, Flow, and Density and Analogous Relation Between Density and Occupancy , 1997 .

[4]  Guan Xu,et al.  Method for Estimating Capacity Reduction in High-Occupancy-Vehicle Lane Ingress and Egress Sections , 1999 .

[5]  Carlos F. Daganzo,et al.  A Pareto Improving Strategy for the Time-Dependent Morning Commute Problem , 1999, Transp. Sci..

[6]  Joy Dahlgren,et al.  High Occupancy Vehicle Lanes: Not Always More Effective Than General Purpose Lanes , 1998 .

[7]  Mark D. Uncles,et al.  Discrete Choice Analysis: Theory and Application to Travel Demand , 1987 .

[8]  Carlos F. Daganzo,et al.  THE CELL TRANSMISSION MODEL, PART II: NETWORK TRAFFIC , 1995 .

[9]  Habib Haj-Salem,et al.  Ramp metering impact on urban corridor traffic: Field results , 1995 .

[10]  D. Klein,et al.  High Occupancy/Toll Lanes: Phasing in Conjestion Pricing a Lane at a Time , 1993 .

[11]  Carlos F. Daganzo,et al.  Spatiotemporal Effects of Segregating Different Vehicle Classes on Separate Lanes , 2008 .

[12]  H. Greenberg An Analysis of Traffic Flow , 1959 .

[13]  Carlos F. Daganzo,et al.  THE NATURE OF FREEWAY GRIDLOCK AND HOW TO PREVENT IT. , 1995 .

[14]  Carlos F. Daganzo,et al.  Using Input-Output Diagram To Determine Spatial and Temporal Extents of a Queue Upstream of a Bottleneck , 1997 .

[15]  Carlos F. Daganzo,et al.  Ten Strategies for Freeway Congestion Mitigation with Advanced Technologies , 2002 .

[16]  Carlos F. Daganzo,et al.  The Smoothing Effect of Carpool Lanes on Freeway Bottlenecks , 2008 .

[17]  G. F. Newell,et al.  Applications of Queueing Theory. , 1983 .

[18]  Chao Chen,et al.  An Empirical Assessment of Traffic Operations , 2005 .

[19]  Michael J. Cassidy Freeway On-Ramp Metering, Delay Savings, and Diverge Bottleneck , 2003 .

[20]  Sam Yagar,et al.  Breakdown-Related Capacity for Freeway with Ramp Metering , 2001 .

[21]  D. Brownstone,et al.  Drivers' Willingness-to-Pay to Reduce Travel Time: Evidence from the San Diego I-15 Congestion Pricing Project , 2002 .

[22]  David A. Hensher,et al.  Influence of vehicle occupancy on the valuation of car driver’s travel time savings: Identifying important behavioural segments , 2008 .

[23]  Carlos F. Daganzo,et al.  Corrigendum to “Urban gridlock: Macroscopic modeling and mitigation approaches” [Transportation Research Part B 41 (2007) 49–62] , 2007 .

[24]  M. Bierlaire,et al.  ESTIMATION OF VALUE OF TRAVEL-TIME SAVINGS USING MIXED LOGIT MODELS , 2005 .

[25]  C A Fuhs HIGH-OCCUPANCY VEHICLE FACILITIES: A PLANNING, DESIGN, AND OPERATION MANUAL , 1990 .

[26]  M. Burris,et al.  Who Chooses to Carpool and Why? , 2007 .

[27]  M J Lighthill,et al.  On kinematic waves II. A theory of traffic flow on long crowded roads , 1955, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[28]  Carlos F. Daganzo,et al.  Effects of high occupancy vehicle lanes on freeway congestion , 2008 .

[29]  C. Daganzo,et al.  Effects of HOV lanes on freeway bottlenecks , 2007 .

[30]  Randall Guensler,et al.  Analysis of Reduction in Effective Capacities of High-Occupancy Vehicle Lanes Related to Traffic Behavior , 2008 .

[31]  B D Greenshields,et al.  A study of traffic capacity , 1935 .

[32]  Ching-Yao Chan,et al.  Preliminary Evaluation of Operational Performance of Different Types of High-Occupancy Vehicle Facilities in California Continuous Access versus Limited Access , 2011 .

[33]  R S Foote,et al.  TRAFFIC FLOW IN TUNNELS , 1958 .

[34]  John W Fischer,et al.  Safe, Accountable, Flexible, Efficient Transportation Equity Act — A Legacy for Users (SAFETEA-LU or SAFETEA): Selected Major Provisions , 2005 .

[35]  Pravin Varaiya,et al.  Freeway performance measurement system (pems) , 2002 .

[36]  Markos Papageorgiou,et al.  Freeway ramp metering: an overview , 2002, IEEE Trans. Intell. Transp. Syst..

[37]  Michael J. Cassidy,et al.  Increasing Capacity of an Isolated Merge by Metering its On-Ramp , 2004 .