Algorithms to Quantify the Impacts of Congestion on Time-Dependent Real-World Urban Freight Distribution Networks Researchers Dr. Miguel FigliozziAssistant Professor, Department of Civil and Environmental Engineering, Portland State University Ryan ConradGraduate Research Assistant, Department of Civil and Environmental Engineering, Portland State University

VRP Solution Algorithm Applications to Real Urban Networks The Solution Algorithm Interfacing with the Google Maps™ API Presentation Overview Portland Case Study Modeling Customer Demands and Constraints Brief Literature Review Objectives/Practical Applications

Brief Literature Review Applying the TDVRP to urban networks  Eglese, Maden, & Slater, (2006): Time-dependent shortest path using modeled road network and Road Timetable™ O-D matrix for solving the TDVRP  Ichoua, S., Gendreau, M., & Potvin, J. (2003): Analyzed Solomon Benchmark Problems with time-dependent arcs; did not include roadway characteristics (e.g. freeways, traffic signals, etc.)  Neither group of researchers looked at routing characteristics Modeling Customer Demands/Constraints  Quak, H. J., & Koster, M. B. M. d. (2009): Analyzed and quantified impacts of public policies on freight carrier and customer costs Portland Transportation Archive Listing (PORTAL)  Bertini, R. L., Hansen, S., Matthews, S., Rodriguez, A., & Delcambre, A. (2005): Overview of Portland’s implementation of an archived data user service (ADUS)

Objectives of Research Research Objectives Provide a reliable solution algorithm for the TDVRP using:  Use historical traffic data  A real urban street network Develop methodology to quantify various customer constraints and demands  Time windows  Delivery time restrictions  Demand levels Improve user interface  Minimize  Data storage  Computational Complexity  User-friendly input/output Assumptions Customer demands and locations known a priori Static problem using historical congestion data

Overview of the Google Maps API Advantages Open-source software available at Very detailed road network Intuitive vehicle routing  Preference for freeways/arterials  Includes roadway characteristics in “free-flow” travel time calculations Additional features allow for selecting customers and plotting routes Very low data requirements/computational complexity Disadvantages Not all code available  Shortest path algorithm  black box Not a time-dependent shortest path calculation Ability to control or reroute vehicles onto alternate routes very limited

Output Customer coordinates Select Customers Map data © Tele Atlas O-D Matrices Output Distance O-D Matrix Output Output Travel Time O-D Matrix Map data © Tele Atlas Interfacing with the Google Maps API Click on the screen to select customers. The first selection is the depot. Uploading customer coordinates… Calculating travel time and distance under free-flow conditions…

VRP Algorithm Speed function Free-flow speeds (O-D Matrices) Optimized routes and performance measures PORTAL Data Travel Time Travel Time Occupancy Occupancy Traffic Volume Traffic Volume Calculate Results Implementing the Google Maps API Optimizing number of routes and total costs…

TDVRP Solution Algorithm TDVRP Algorithm* H c and H y algorithms calculate expect arrival and departure times among feasible routes Accept network-wide TDTTs, but must be modified to accept travel times from multiple locations/data sources Auxiliary Routing Algorithm Route Construction Algorithm Route Improvement Algorithm Service Time Improvement Algorithm * Reference: Figliozzi, M.A., A Route Improvement Algorithm for the Vehicle Routing Problem with Time Dependent Travel Times. Proceeding of the 88th Transportation Research Board Annual Meeting, Washington DC. USA, January Route ConstructionRoute Improvement

TDVRP Solution Algorithm Arrival and Departure Time Algorithms H yf and H yb calculate vehicle travel times Traffic queuing effects captured by H yq algorithm Auxiliary Routing Algorithm Route Construction Algorithm Route Improvement Algorithm Service Time Improvement Algorithm Arrival Time Algorithm Departure Time Algorithm PORTAL Data Occupancy Vehicle Flow Google Maps API Free-flow Travel Speeds PORTAL Data Congested Travel Speeds

TDVRP Solution Algorithm Concept of Traffic Bottlenecks

TDVRP Solution Algorithm Modeling Traffic Conditions PORTAL Data  Obtained from detector loop stations on I-5 freeway  Travel time and speed data Traffic Bottlenecks  Areas where travel speed is reduced Speed calculated by API

TDVRP Solution Algorithm Modeling Traffic Conditions PORTAL Data Obtained From Detector Loop Stations on I-5  Traffic flow  Occupancy Used to simulate traffic queuing Occupancy Flow Vehicle queuing

TDVRP Solution Algorithm 10%

Case Study: Portland, OR Challenges  Growing traffic congestion  Diverse customer types in CBD  Time-sensitive deliveries (e.g. time windows)  Vehicle restrictions

Case Study: Portland, OR Carrier Responses  Shifting Afternoon Deliveries to Early Morning  Employing Additional Drivers/Vehicles  Contracting Deliveries

Modeling Customer Demands and Constraints Customer and Depot Selection  Customers selected by zoning criteria; 100 total  Two depot locations  Central location  Suburban location  Instances: random selections of customer to simulate day-to-day changes in deliveries

Central Depot Customers with service time constraints Central Depot Suburban Depot

Modeling Customer Demands and Constraints Constraints  Early-morning delivery period  Mixed-use and residential: no deliveries before 7AM  1 hr. time windows; no time windows for residential  Extended morning delivery  Extended 2 hrs.  1.5 hr. time windows (except residential)  Congestion begins to intensify

Some Results Congested vs. Non-congested Traffic Conditions  Static traffic bottlenecks: small differences in travel time, vehicles required, etc.  Dynamic (with traffic queuing effects) and suburban depot  Significant increase in the number of vehicles required  Significant increase in travel distance  Almost four-fold increase in travel times Depot Location  Location matters: Greater increases in travel time, distance and vehicles for suburban depots compared to central locations.

Acknowledgements Myeonwoo Lim, Computer Science Graduate Student, Portland State University Nikki Wheeler, Civil Engineering Graduate Student, Portland State University Oregon Transportation Research and Education Consortium (OTREC)

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