Truck Tour Modeling Using GPS data

Transcription

1 Truck Tour Modeling Using GPS data

2 2 Background Introduction Framework for Analysis Tool to Process Clean Truck GPS data Description of Clean Truck Tour Results Tour Based Model Tour Based Entropy Maximization Model Selective Vehicle Routing Problem Conclusions

3 3 Freight Transportation Modeling Mostly trip based model (demand) used Does not address extensive trip chaining behaviors in freight truck movement Move toward to innovative freight model including tour based model Efforts to complement (support) conventional surveys with GPS data Research using Truck GPS data (Greaves and Figliozzi (2008);Sharman and Roorda (20);You and Ritchie(202)) Capability of capturing truck trajectories over many years Readily update Truck Activity Models using frequently collected GPS data GPS data available under the SPBP clean truck program Our approach for improving Freight Transportation Model Phase : Trip based Model (equivalent information) + GPS data Tour based Model Phase 2: Inverse selective vehicle routing problem + GPS data Tour based Model (+)

4 4 Issues What is distinct feature of Port truck movements? Phase : How can we re-generate a tour-based model from commonly available trip-based model? Phase 2: What are the objectives and constraints of a multi-objective vehicle routing problem such that observed truck patterns are optimal? Objectives Introduce distinct characteristics of the port truck movements Utilize Tour-Based Entropy Maximization Model using GPS data Discuss the conceptual formulation for the (Inverse) Selective Vehicle Routing Problems

5 5 Freeway System The I-70 and the I-0 along with cross freeways Railroad System The Alameda corridor rail link and a number of rail yards CTP trucks movements year GPS data from 540 Trucks Almost 60% of GPS installed CTP trucks movements in study area 40% of trucks movements : Oakland, Sacramento, Riverside, San Bernardino, Chino, San Diego

6 6 Input: Raw GPS data Geocode GPS data Identify Truck Depots Delete false-positive stops output Stop Trip # depot Trip # Trip #2 Trip depot # n+ Trip # depot Trip # n+ depot Trip # n+ Trip # n+ Trip # n Stop n n Stop Stop n Stop Trip #2 Trip #2 2 3 Trip #2 Trip #3 2 3 Stop 2 Trip #3 2 Stop 3 3 Trip #3 2 3 Stop Stop 2 Stop 3 Stop n Stop 2 Stop 3 Stop 2 Trip #3 Stop 3 n Stop n You and Ritchie (202)

7 7 Observed Trip Chain Behaviors of Port CTP Trucks The CTP trucks operate an average of.7 tours per day (equivalent to 6.2 trips per day) Approximately 3. stops per tour Due to use of drayage trucks which are often involved in lengthy loading/unloading of containers in and out of SPBP and at each stop Distribution of Tour Travel Time ( year data) Distribution of Tour Distance ( year data)

8 8 Overall Tour Characteristics Clean trucks at the SPBP rely on daily based operations Identified four tour types, but individual tours are very diverse due to the combination by visiting locations and times. To better capture all possible tour sets recommended long term data collection Recursive patterns in a tour (Type B, C, and D) <Type A> 34.36% <Type C> depot 2 3 n <Type B> 44.7% depot depot 3.% depot S * S * <Type D> 8.35% S * 2 n n S * S * S * 2 S *

9 9 Capable to address trip chain behaviors Most of these approaches at the disaggregate level by solving a vehicle routing problem* or by using the probabilities generated by a set of discrete choice models** * Wisetjindawat et al. 2007; Donnelly 2007 ** Stefan et al. 2005; Gliebe et al. 2007; Hunt and Stefan 2007 expensive procedures such as collecting travel diary data, the long computational time, and less reliance on behavioral assumptions In Contrast, three types of combined network equilibrium model(maruyama and Harata 2005); Tour based Entropy Maximization Model (Wang and Holguin-Veras 2008) The recursive tour pattern of CTP trucks is hard to address with the conventional entropy maximization.

10 0 Concept To better capture recursive patterns in a tour, Add a constraint (Lk)implying the direction of movements Truck tour from a year worth GPS data (from Jan-Dec, 200) Formulation The formulation of the first-order condition (Karush-Kuhn-Tucker (KKT) conditions) is the Lagrange function as follows: N K x j = exp λ i a ij + λ 3 k l kj + β c Tj + β 2 c Hj i= k=

11 Calibration using the Primal Dual Method for Convex Optimization (PDCO) algorithms Measures Of Accuracy : MAPE * Estimated Result A Year Level * Level 2** Level 3*** Without Directional Constraints 55.00% 40.88% 35.32% 43.4% With Directional Constraints 3.53% 6.9% 4.06% 8.2% Number of Tours (total number of journeys) 33,48 (83,694) 0,23 (24,224) 9,368 (9,660) 7,43 (39,80) Tour-travel time related Lagrange multipliers (β ) Tour-Transaction time related Lagrange multipliers (β 2 ) *) MAPE: Mean Absolute Percentage Error **) January, February, March and April ***) May, November, and December ****) June, July, August, September, and October The observed tour length distributions

12 2 Issue Tour Based Entropy Maximization Model: limited to long term Forecasting Conventional disaggregate approach (p. 9): hard to be duplicated since coefficients can be changeable in the different cases Concept Multi-Objective Selective Vehicle Routing Problem Minimize Z = β i T Z i (X, Ψ, T) 5 i=0 Choose a subset of activities to maximize utilities by visiting many locations including multiple visits while Minimizing disutilities (Travel Time, Emissions based on travel distance and speed bin, etc. ) and ensuring that spatio-temporal constraints are met. A normative model that can be calibrated with GPS data

13 3 By solving the Inverse selective vehicle routing problem, Determine set of coefficient for utilities and travel disutilities for a given firm such that the observed pattern using GPS data are optimal. Inverse Optimization Approach A theory that seeks out a set of model parameters such that observed decision variables are optimal (Tarantola, 2005). Effectively formulated for an linear programming and network flow problem (Burton and Toint, 992; Ahuja and Orlin, 200) and for mixed integer linear programming problem (Wang, 2009)) Solved Household activity pattern problem using Inverse optimization approach (Chow and Recker, 20)

14 4 Distinct characteristics of the CTP port truck movements (Type A-D) Utilize Tour-Based Entropy Maximization Model using GPS data Considered the direction of freight movements (adding a constraint (Lk)) Best use for converting a trip based model to a tour based model Even without a full truck diary (Hunt and Stefan, 2007), the Inverse Selective Vehicle Routing Problem would be possible to build an urban truck distribution model using only GPS data.

15 Special Thanks 5 Caltrans, Office of Travel Forecasting and Analysis, Division of Transportation System Information Sarah Chesebro, Chief, OTFA Doug Maclvor, Chief, Transportation/Freight Modeling and Data Branch Chad Baker, Kalin Pacheco, Dian Jacobs University of California Transportation Center for research support Port of Long Beach Eric C. Shen, the director of Transportation Planning Iris You: soyoungy@uci.edu