Algorithms to Quantify the Impacts of Congestion on Time-Dependent Real-World Urban Freight...
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Algorithms to Quantify the Impacts of Congestion on Time-Dependent Real-World Urban Freight Distribution Networks Researchers Dr. Miguel FigliozziAssistant
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
Slide 2
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
Slide 3
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 Portlands implementation of an archived data
user service (ADUS)
Slide 4
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
Slide 5
Overview of the Google Maps API Advantages Open-source software
available at http://code.google.com/ 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
Slide 6
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
Slide 7
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
Slide 8
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 2009. Route ConstructionRoute Improvement
Slide 9
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
Slide 10
TDVRP Solution Algorithm Concept of Traffic Bottlenecks
Slide 11
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
Slide 12
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
Slide 13
TDVRP Solution Algorithm 10%
Slide 14
Case Study: Portland, OR Challenges Growing traffic congestion
Diverse customer types in CBD Time-sensitive deliveries (e.g. time
windows) Vehicle restrictions
Slide 15
Case Study: Portland, OR Carrier Responses Shifting Afternoon
Deliveries to Early Morning Employing Additional Drivers/Vehicles
Contracting Deliveries
Slide 16
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
Slide 17
Central Depot Customers with service time constraints Central
Depot Suburban Depot
Slide 18
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
Slide 19
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.
Slide 20
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)