Integrative Freight Demand Management in the New York City Metropolitan Area

This project is one of the first in the world that has successfully integrated the use of remote sensing technology—in this case Global Positioning System (GPS) enabled cell phones—as part of a system that effectively reduces truck traffic in the congested hours of the day, through the use of incentives to receivers. In doing so, the project designed, developed, and pilot tested a concept that: Exploited the use of GPS technology and its estimates of travel times and delays, for compliance verification, data sharing among participating partners, and validation of the traffic models used to predict the effects of the proposed program on the traffic network; Developed state of the art analytical formulations and simulation systems to study and predict the behavior of carriers and receivers—together with the underlying behavioral theories—that were successfully verified during the pilot test conducted; Led to new policy paradigms that, by exploiting the nature of Large Traffic Generators and unassisted deliveries, greatly reduce the need for financial incentives to receivers; Garnered the enthusiastic support of large corporations involved in urban delivery activities, trade organizations, trade publications, and the industry at large, as they understood the concept‘s potential as a business-friendly and effective freight demand management tool they could embrace; Conducted institutional analyses to identify and preliminarily discuss potential inter-agency arrangements that could support the concept; and Has received considerable research acclaim.

[1]  José Holguín-Veras,et al.  Effectiveness of Financial Incentives for Off-Peak Deliveries to Restaurants in Manhattan, New York , 2006 .

[2]  S. Washington,et al.  Statistical and Econometric Methods for Transportation Data Analysis , 2010 .

[3]  Xuegang Ban,et al.  Vehicle Trajectory Reconstruction for Signalized Intersections Using Mobile Traffic Sensors , 2013 .

[4]  George F. List,et al.  Best-Practice Truck-Flow Estimation Model for the New York City Region , 2002 .

[5]  Kara M. Kockelman,et al.  Simulation-Based Heuristic Approach for Dynamic Marginal Cost Pricing , 2008 .

[6]  José Holguín-Veras,et al.  Erratum to: Approximation model to estimate joint market share in off-hour deliveries , 2012, Logist. Res..

[7]  James P McCarthy,et al.  Traffic Analysis Toolbox , 2005 .

[8]  Julian Allen,et al.  Night-Time Delivery Restrictions: A Review , 2006 .

[9]  M J Maher,et al.  A comprehensive methodology for the fitting of predictive accident models. , 1996, Accident; analysis and prevention.

[10]  José Holguín-Veras,et al.  Freight Generation, Freight Trip Generation, and Perils of Using Constant Trip Rates , 2011 .

[11]  Fang Yuan,et al.  INCIDENT DETECTION USING SUPPORT VECTOR MACHINES , 2003 .

[12]  Kaan Ozbay,et al.  Evaluating Highway Capacity Investments using a GIS -based tool - Trip -based Full Marginal Cost Approach , 2007 .

[13]  George F. List,et al.  Off-Peak Freight Deliveries: Challenges and Stakeholders' Perceptions , 2005 .

[14]  J. Holguín-Veras Necessary conditions for off-hour deliveries and the effectiveness of urban freight road pricing and alternative financial policies in competitive markets , 2008 .

[15]  Xiaolei Ma,et al.  Developing a GPS-Based Truck Freight Performance Measure Platform , 2010 .

[16]  Kaan Ozbay,et al.  Evaluation of Impacts of Time-of-Day Pricing Initiative on Car and Truck Traffic: Port Authority of New York and New Jersey , 2006 .

[17]  D. McFadden,et al.  URBAN TRAVEL DEMAND - A BEHAVIORAL ANALYSIS , 1977 .

[18]  José Holguín-Veras,et al.  An Investigation on the Effectiveness of Joint Receiver–Carrier Policies to Increase Truck Traffic in the Off-peak Hours , 2008 .

[19]  Kaan Ozbay,et al.  Value of Travel Time Estimation Using Hierarchical Bayesian Mixed Logit Approach , 2010 .

[20]  Byung-Wook Wie,et al.  Dynamic Stackelberg equilibrium congestion pricing , 2007 .

[21]  José Holguín-Veras,et al.  An Investigation on the Effectiveness of Joint Receiver–Carrier Policies to Increase Truck Traffic in the Off-peak Hours , 2007 .

[22]  Heng Wei,et al.  A Feedback-Based Dynamic Tolling Algorithm for High-Occupancy Toll Lane Operations , 2008 .

[23]  D C Roberts INSTITUTIONS FOR TRANSPORTATION OPERATIONS WITH RECOMMENDATIONS FOR REAUTHORIZATION , 2001 .

[24]  Kaan Ozbay,et al.  AN ASSESSMENT OF METHODOLOGICAL ALTERNATIVES FOR A REGIONAL FREIGHT MODEL IN THE NYMTC REGION , 2001 .

[25]  José Holguín-Veras,et al.  Modeling commercial vehicle empty trips with a first order trip chain model , 2003 .

[26]  José Holguín-Veras,et al.  ESTIMATION OF FREIGHT TRIP GENERATION BASED ON LAND USE , 2011 .

[27]  José Holguín-Veras,et al.  Optimal distribution of financial incentives to foster off-hour deliveries in urban areas , 2012 .

[28]  Jan Fabian Ehmke,et al.  Floating car based travel times for city logistics , 2012 .

[29]  H. Mahmassani,et al.  Toll Pricing and Heterogeneous Users , 2005 .

[30]  D. Shoup The High Cost of Free Parking , 1997 .

[31]  Jeffrey Short,et al.  Developing a Methodology for Deriving Cost Impacts to the Trucking Industry that Generate from Freight Bottlenecks , 2010 .

[32]  A. C. Pigou Economics of welfare , 1920 .

[33]  Kaan Ozbay,et al.  Estimation of Truck Volumes and Flows , 2004 .

[34]  H Lum,et al.  Modeling vehicle accidents and highway geometric design relationships. , 1993, Accident; analysis and prevention.

[35]  E. Sullivan,et al.  CONTINUATION STUDY TO EVALUATE THE IMPACTS OF THE SR 91 VALUE-PRICED EXPRESS LANES. FINAL REPORT , 2000 .

[36]  H. Quak Sustainability of Urban Freight Transport: Retail Distribution and Local Regulations in Cities , 2008 .

[37]  R Thomas TRIP DISTRIBUTION MODELS. , 1970 .

[38]  Peter Gordon,et al.  Estimating Freight Flows for Metropolitan Area Highway Networks Using Secondary Data Sources , 2010 .

[39]  Hsun-Jung Cho,et al.  Time Dependent Origin-destination Estimation from Traffic Count without Prior Information , 2009 .

[40]  Jianhe Du,et al.  Increasing the accuracy of trip rate information from passive multi-day GPS travel datasets: Automatic trip end identification issues , 2007 .

[41]  Jie Lin,et al.  Moves versus Mobile , 2011 .

[42]  J. Beardwood,et al.  The shortest path through many points , 1959, Mathematical Proceedings of the Cambridge Philosophical Society.

[43]  Hani S. Mahmassani,et al.  A bi-criterion dynamic user equilibrium traffic assignment model and solution algorithm for evaluating dynamic road pricing strategies , 2008 .

[44]  A. Walters The Theory and Measurement of Private and Social Cost of Highway Congestion , 1961 .

[45]  Jose Holguin-Veras,et al.  Potential for Off-Peak Freight Deliveries to Congested Urban Areas (TIRC Project C-02-15) , 2006 .

[46]  P O Roberts,et al.  URBAN FREEWAY GRIDLOCK STUDY: DECREASING THE EFFECTS OF LARGE TRUCKS ON PEAK-PERIOD URBAN FREEWAY CONGESTION , 1990 .

[47]  Varanesh Singh,et al.  Microsimulation Model Design in Lower Manhattan: A Street Management Approach , 2009 .

[48]  Holguín-Veras,et al.  Urban Delivery Industry Response to Cordon Pricing, Time-Distance Pricing, and Carrier-Receiver Policies , 2011 .

[49]  Jose Holguin-Veras,et al.  On the Overall Performance of Comprehensive Policies to Manage Truck Traffic in Congested Urban Areas , 2006 .

[50]  K. Ogden Urban Goods Movement: A Guide to Policy and Planning , 1991 .

[51]  E C Noel,et al.  SURVEY OF OFF-HOURS DELIVERY , 1980 .

[52]  Sanders LOCAL LAWS OF THE CITY OF NEW YORK , 2010 .

[53]  George Yannis,et al.  Effects of Urban Delivery Restrictions on Traffic Movements , 2006 .

[54]  José Holguín-Veras,et al.  Off-Hour Deliveries in Manhattan, New York City , 2011 .

[55]  Edward McCormack,et al.  ITS Devices Used to Collect Truck Data for Performance Benchmarks , 2006 .

[56]  Daniël Wedema Games And Information An Introduction To Game Theory 3rd Edition , 2011 .

[57]  Satish V. Ukkusuri,et al.  Overall Impacts of Off-Hour Delivery Programs in New York City Metropolitan Area , 2011 .

[58]  What TransCAD Users Should Know about New Static Traffic Assignment Methods , 2010 .

[59]  Miguel A. Figliozzi,et al.  Collecting Commercial Vehicle Tour Data with Passive Global Positioning System Technology , 2008 .

[60]  Mark A. Turnquist,et al.  Estimating truck travel patterns in urban areas , 1994 .

[61]  J. Holguín-Veras,et al.  The impacts of time of day pricing on the behavior of freight carriers in a congested urban area: Implications to road pricing , 2006 .

[62]  Wenjuan Zhao,et al.  Truck travel time reliability and prediction in a port drayage network , 2011 .

[63]  Soeren Kjaersgaard,et al.  Sustainable city logistic solutions , 2004 .

[64]  Radford M. Neal Pattern Recognition and Machine Learning , 2007, Technometrics.

[65]  R. Tobin,et al.  Dynamic congestion pricing models for general traffic networks , 1998 .

[66]  Oscar Franzese,et al.  Estimating the Impact of Pickup- and Delivery-Related Illegal Parking Activities on Traffic , 2005 .

[67]  Yafeng Yin,et al.  Dynamic Tolling Strategies for Managed Lanes , 2009 .

[68]  B. Warf,et al.  THE PORT AUTHORITY OF NEW YORK-NEW JERSEY * , 1988 .

[69]  José Holguín-Veras,et al.  Trucking Costs: Comparison Between Econometric Estimation and Cost Accounting , 2008 .

[70]  Teodor Gabriel Crainic,et al.  Demand Matrix Adjustment for Multimodal Freight Networks , 2001 .

[71]  Timothy C. Coburn,et al.  Statistical and Econometric Methods for Transportation Data Analysis , 2004, Technometrics.

[72]  José Holguín-Veras,et al.  Evaluation Study of Port Authority of New York and New Jersey's Time of Day Pricing Initiative , 2005 .

[73]  John K Kruschke,et al.  Bayesian data analysis. , 2010, Wiley interdisciplinary reviews. Cognitive science.

[74]  Bradley P. Carlin,et al.  Bayesian measures of model complexity and fit , 2002 .

[75]  P.H.L. Bovy,et al.  Dynamic road pricing for optimizing network performance with heterogeneous users , 2005, Proceedings. 2005 IEEE Networking, Sensing and Control, 2005..

[76]  D Wild Best Urban Freight Solutions (BESTUFS) , 2006 .

[77]  Petros A. Ioannou,et al.  MODELING AND ROUTE GUIDANCE OF TRUCKS IN METROPOLITAN AREAS , 2001 .

[78]  T. Crainic,et al.  ADVANCED FREIGHT TRANSPORTATION SYSTEMS FOR CONGESTED URBAN AREAS , 2004 .

[79]  T. Friesz,et al.  A Computable Theory of Dynamic Congestion Pricing , 2007 .

[80]  Praveen Edara,et al.  A real-time road pricing system: The case of a two-link parallel network , 2007, Comput. Oper. Res..

[81]  Hugh Finlay,et al.  Noise Abatement and Night Deliveries , 2008 .

[82]  P. Vilain,et al.  Value Pricing and Freight Traffic: Issues and Industry Constraints in Shifting from Peak to Off-Peak Movements , 2000 .

[83]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[84]  M. Baucus Transportation Research Board , 1982 .

[85]  Satish V. Ukkusuri,et al.  Mesoscopic Simulation Evaluation of Dynamic Congestion Pricing Strategies for New York City Crossings , 2011 .

[86]  José Holguín-Veras,et al.  Behavioral Microsimulation Formulation for Analysis and Design of Off-Hour Delivery Policies in Urban Areas , 2009 .