Applying GPS Data to Understand Travel Behavior, Volume I: Background, Methods, and Tests (2014) / Chapter Skim
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52 Introduction Chapter 3 summarizes the results from the tests conducted on the methods identified in Chapter 2. This demonstration was conducted through two experiments: • Experiment A: augmenting person-based GPS HTS data with trip details.
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53 Figure 3-1. Overall sequence of steps covered in Experiment A
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54 The remainder of this chapter is organized as follows. First, it introduces the data sets used to carry out the selected experiments along with an outline of the implementation and testing approaches used to evaluate the methods.
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55 The resulting filtered points from the application of each method were compared against the processed and filtered points in the original GPS data deliverable, also referred to as the reference data, which were reviewed by GeoStats analysts. The null hypothesis here was that each point's final filtered state was to be the same in both data sets.
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56 Figure 3-3 illustrates the results of the three tested methods using a sample of the points from one of the processed files, which contained activity in downtown Atlanta. The maps show points that were classified as noise by the various methods, with the shade of gray varying based on each point's instantaneous speed (converted to miles per hour)
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57 point every 3 s, and only points with instantaneous speeds above 1 mph were recorded. Success of the method was measured by comparing the detected trips against the trips in the reference data set.
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58 end times that were within 15 min of each other. The two following errors were detected from this check: • Type I error: Mode segment end point is not found in method but is found in reference data.
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59 Source References Implementation Findings Type I Error Type II Error Tsui and Shalaby (2006) and Schüssler and Axhausen (2008)
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60 Note that Type I and Type II are paired for this hypothesis (picking a wrong answer implies that you failed to pick the right answer) , but it is still interesting to treat them separately to see what modes are frequently overused (Type I)
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61 Method Types Source References Implementation Findings Heuristics Stopher, Clifford, and Zhang (2007) This method needed baseline statistics for each mode, so the 180 training trips were used.
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62 Ref\Method Walk Bicycle Auto Bus Heavy Rail N/A Type II Walk 14 4 0 0 0 – 4 (22%) Bicycle 2 16 0 0 0 – 2 (11%)
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63 choices. This additional review may be automated through the use of GIS transit infrastructure data, which helped lower the Type I error rate of the heuristics method for bus and heavy rail modes.
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64 hold members on the trip was reduced by one)
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65 During the model's initial successful estimation runs, a large number of the estimated coefficients either failed the null hypothesis t-test or ended up with coefficient estimates for which BIOGEME could not estimate p-values. Furthermore, two purposes (#12: all other activities at school and #24: attend major sporting event)
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66 model results (Type II error) , with the first (#22)
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Note: agg = aggregate, dis = disaggregate. Figure 3-5.
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68 J48. In addition to the life-cycle Boolean variables listed in Table 3-13, the computed variables identified in Table 3-14, and the spatial variables in Table 3-15, the arrival hour was added to the list of inputs available to the tree-building algorithm.
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69 Figure 3-8 shows the actual trip purpose frequencies according to the selections identified by the decision tree. Match rates are noted as percentages on top of the choice bars.
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70 would be to define the minimum set of purposes that can be easily explained to survey participants in the calibration subsample and yet still provides enough resolution for travel demand modeling. • Plan to collect personal locations (e.g., work, school, and volunteer)
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71 Some assumptions regarding the input data were necessary for model estimation and should hold for any data set to which the model is applied. These include: • The GPS traces can be uniquely linked to one person.
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72 Stage 1 analysis shown in Figure 3-2. During the completion of the first stage of Experiment B, several subtasks were performed.
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73 Variable Name Description Avg Min Max total_tours Number of total tours per day 2.850 1 13 num_subtours Number of subtours per day 0.017 0 2 work_tours Number of work tours 0.743 0 4 school_tours Number of school tours 0.303 0 5 other_tours Number of other tours 1.804 0 13 avg_stops_per_tour Average number of stops per tour 2.351 1 12 avg_stops_per_work_tour Average number of stops per work tour 1.070 0 10 avg_stops_per_school_tour Average number of stops per school tour 0.308 0 8 avg_stops_per_other_tour Average number of stops per other tour 1.689 0 12 avg_tour_ttime Average travel time per tour 57.843 0 841 avg_work_tour_ttime Average travel time per work tour 32.233 0 1,050 avg_school_tour_ttime Average travel time per school tour 6.029 0 423 avg_other_tour_ttime Average travel time per other tour 35.903 0 1,160 at_home_duration Total time spent at home 1970.328 0 8,571 num_acts_work Number of work activities 0.775 0 6 total_dur_work Total duration of all work activities 377.092 0 2,878 avg_dur_work Average duration of work activities 208.874 0 1,439 num_acts_school Number of school activities 0.328 0 6 total_dur_school Total duration of all school activities 123.748 0 2,878 avg_dur_school Average duration of school activities 65.177 0 1,439 num_acts_pickdrop Number of pickup/drop-off activities 0.234 0 10 total_dur_pickdrop Total duration of all pickup/drop-off activities 1.418 0 90 avg_dur_pickdrop Average duration of pickup/drop-off activities 0.587 0 20 num_acts_other Number of other activities 3.920 0 32 total_dur_other Total duration of all other activities 290.168 0 2,878 avg_dur_other Average duration of other activities 79.994 0 1,439 auto_total Percentage of tours by auto mode 0.769 0 1 auto_work Percentage of work tours by auto mode 0.326 0 1 auto_school Percentage of school tours by auto mode 0.095 0 1 Table 3-20. Processed person-level travel characteristics.
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74 between educational attainment and work status. The education sub-models similarly conform to expectations.
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75 Model Application The various models estimated for the person and household characteristics have been applied to both the Chicago data set, which was used as the training data, and the Portland household survey data set. The latter was processed using the data processing procedure and combined with the censustract land use variables.
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76 constants in the model are fitted to the Chicago data. However, a calibration process could be undertaken when applying the estimated model to other areas where the alternative specific constants for each person type could be adjusted until a known distribution of the person types is matched.
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77 The results of the gender classification model for Chicago and Portland are shown in Table 3-28. The gender classification model was a simple binary logit model.
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78 Table 3-27. Ordered logit age category model results for training and test data.
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79 substantial increase, showing that the presence of children is related to differences in travel patterns, as expected. The remaining sub-models for person attributes perform similarly.
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80 the reverse case. In other words, people travel the way they do because of who they are, but people are generally not who they are because of the way they travel (with some exceptions, such as with possession of a license or vehicle ownership)
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