Urban Freight Delivery Stop Identification Using GPS Data
Xia Yang
Department of Civil and Environmental Engineering
Rensselaer Polytechnic Institute, 110 Eighth Street, Room JEC 5107, Troy, NY 12180-3590
Email: yangx5@rpi.edu
Zhanbo Sun
Department of Civil and Environmental Engineering
Rensselaer Polytechnic Institute, 110 Eighth Street, Room JEC 5107, Troy, NY 12180-3590
Email: sunz2@rpi.edu
Xuegang (Jeff) Ban, Ph.D. *
Department of Civil and Environmental Engineering
Rensselaer Polytechnic Institute, 110 Eighth Street, Room JEC 4034, Troy, NY 12180-3590
Phone: (518) 276-8043 Fax: (518) 276-4833 Email: banx@rpi.edu
*Corresponding Author
José Holguín-Veras, Ph.D., P.E.
Department of Civil and Environmental Engineering
Rensselaer Polytechnic Institute, Room JEC 4030, Troy, New York, 12180
Email: jhv@rpi.edu
Number of Words: 4,600 words + 8 Figures + 3 Tables = 7,350 words+18 references
Submitted for presentation and publication to the Transportation Research Board (TRB) 93rd
Annual Meeting, Washington, D.C.
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Abstract
Delivery stop identification is a crucial but challenging step in urban freight performance
measurement using second-by-second GPS data. This paper presents the application of a robust
learning method, i.e. Support Vector Machine (SVM), in identifying delivery stops using GPS
data. Stop duration, the distance to the center of the city, and the binary distance to a stop’s
closest bottleneck are extracted as the three major features used in the SVM model. A linear
SVM with nested K-fold cross-validation proves to be highly reliable and robust in delivery stop
identification, in spite of the imbalance in the number of delivery stops and non-delivery stops. A
case study is conducted using second-by-second GPS data in New York City. The identification
accuracy for the case study is very high and the average error rates are only around 0.2% for both
the training and testing data sets, resulting in only 3 stops misidentified among the 2249 stops in
total.
Keywords: Freight Performance Measurement; GPS; Delivery Stop Identification; Support
Vector Machine (SVM); Nested K-Fold Cross-Validation
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Introduction
Freight performance measurement is intended to assist public agencies and private freight
shipping companies to monitor and improve freight performances pertaining to mobility, energy
and environment impacts. In this paper, the focus is on urban freight that mainly delivers
commodities to various locations in urban centers from warehouses. The unique feature of urban
freight is therefore “tour-centric.” Here a tour refers to an entire urban freight trip, starting at a
warehouse, making multiple deliveries/pickups in the middle, and ending usually at the same
warehouse. The deliveries or pickups usually take significant amount of time (from several
minutes to a few hours), which are called “delivery stops” in this paper. Correctly identifying
delivery stops is essential to characterize urban freight, such as tours, tour durations, delivery
times, number of stops, among others. Traditionally, delivery stop identification was done via
surveys or driver logs, which is time/resource consuming and can only cover limited urban areas.
Global Positioning System (GPS) data has recently gained popularity in measuring freight
performances because of the advantages of GPS units over traditional tools such as loop
detectors and traffic cameras. Instead of gathering data from only a finite number of network
locations, GPS units can capture continuous vehicle traces. Besides, GPS units are much smaller
and less expensive than traditional data gathering tools. However, GPS data also has its own
limitations, e.g., uncertainty about vehicle types (although vehicle class may be inferred using
traces, see Sun and Ban (2013)), signal loss and spatial inaccuracy caused by urban canyoning
(i.e. tunnels, tall buildings, etc.), difficulties for data cleaning (McCormack, et al., 2010; Greaves
and Figliozzi 2008; Du and Aultman-Hall, 2007), and so on.
Identifying urban freight delivery stops using GPS data proves to be challenging. Until recently
the only data provided by GPS units indicating that a vehicle may have stopped is the absence of
any communications between the satellite and the receiver (pings). For example, if the vehicle
speed drops below certain value, there will be no pings, indicating a possible vehicle stop. As a
result, it may be difficult to make a distinction between vehicle stops (i.e. traffic stops, deliveries,
etc.) and signal loss. Furthermore, even after a vehicle stop can be properly determined, one still
needs to distinguish between the delivery stops and non-delivery stops. To this end, proper
algorithmic techniques need to be applied.
One of the first attempts to discuss the stop identification issue was Du and Altman-Hall (2007),
who analyzed GPS data collected from a subset of travelers in Lexington, KY between March
2002 and July 2003 and calibrated it with manual travel logs provided by the participants. Using
the two data sets, they identified the heading change (compass direction), dwell time (time
elapsed while vehicle speed drops below a certain level), the distance between the GPS points
and the network geometry as the main parameters for trip end prediction. It was found that the
proper benchmarks for detecting trip ends were a heading change of 180 degrees, and/or a dwell
time between 20 and 140 seconds. The study however focused on passenger cars. Since
passenger cars typically take less time to park, it is very likely that a lower threshold for vehicle
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dwell times is acceptable for identifying a trip end of passenger cars. This may not be the case
for commercial vehicles.
Recently, Greaves and Figliozzi (2008) developed an algorithm to identify the stops for
commercial vehicles. The algorithm analyzed the time difference between GPS-to-satellite
communications to determine if the vehicle was stopped. By investigating different thresholds
between 120 seconds and 300 seconds, it was found that 240 seconds was an adequate threshold
to indicate a stop. In addition to the time threshold, the geographic distance between the
locations of a vehicle at consecutive communications was also considered. If the vehicle had
moved more than the accuracy rating of the device (e.g., 6 meters), it was determined that the
signal had been lost. They also tagged any points where the vehicle position changed less than 6
meters regardless of the time elapsed to identify short stops by manual inspection. The limitation
of their algorithm is that many of the results rely on manual inspections, which may be biased
and also time-consuming for a large dataset. Their algorithm becomes further distressed in urban
areas such as the New York City where a commercial vehicle may have to move several times
for the same delivery due to the extremely demanding spatial constraints of the Manhattan traffic
network (e.g., numerous one-way streets, tall buildings and pedestrian traffic). Since the
algorithm needs to flag such short trips for manual inspection, it may end up manually checking
every delivery site.
More recently McCormack et al. (2010) analyzed data from the Seattle, Washington
metropolitan area using an algorithm that recorded delivery stops when the vehicle’s dwell time
(i.e. time that the vehicle engine is off or idle) exceeded 3 minutes (180 seconds). It was pointed
out that in addition to insignificant truck movement, the GPS points tend to fluctuate when a
truck idles. To deal with this issue, if the distance between two consecutive data points was less
than 65 feet, this instance will be removed. While this algorithm is effective at filtering spurious
trips, it also removes data that could be significant for other freight performance measures such
as service times (i.e., how long it takes for the truck to unload and start the next trip).
The purpose of this paper is to develop a robust method to identify urban freight delivery stops
using fine-grained (second-by-second) GPS data. The data were collected from a trucking
company (the name of the company cannot be released due to the non-disclosure agreement
signed by the research team with the company), delivering groceries to multiple stores in New
York City especially Manhattan. The key technique applied in the algorithm is Support Vector
Machine (SVM). SVM is a recently developed classifier, which can be used for two-group or
multiple-group classification (Vapnik1995; Bishop 2006). In transportation, SVM has been used
for incident detection, signal timing estimation, vehicle classification, regression analysis, and so
on (Corinna and Vladdimir, 1995; Fang and Ruey, 2003; Hao et al., 2012; Sun and Ban, 2013).
A three-stage algorithm is developed based on linear SVM with nested K-fold cross-validation,
which is effective in dealing with the significant imbalance in the distribution of classes
(Alexander S., et al, 2005.). The first stage is to identify all the stops based on speeds as recorded
in the second-by-second GPS data. In the second stage, three features about stops are extracted,
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including stop duration, distance to the center of the city, a binary distance to the closest
bottleneck such as tunnel, toll booth, and so on. The third stage is the implementation of the
linear SVM with nested K-fold cross-validation to classify all stop into delivery stops and nondelivery stops. The classification results based on this three-stage algorithm are satisfactory with
only three stops misidentified among a total of 2,249 stops and the average misclassification
rates are around 0.2% for both the training and testing datasets.
Introduction of SVM
SVM is a pattern classifier (Vapnik 1995; Corinna and Vladdimir V., 1995). It first represents
training data in space so that the points are divided by a hyperplane with the largest separation or
margin between the two classes. Such a hyperplane is known as the maximum-margin
hyperplane. Then the testing data is mapped into the same space and predicted to belong to
which side of the separating hyperplane. For non-linearly separable data, the SVMs uses the
kernel method to transform the original input space into a higher dimensional feature space
where an optimal linear separating hyperplane is constructed (Fang and Ruey, 2003). Since the
linear SVM for two-group classification is sufficiently accurate in identifying urban freight
delivery stops, only linear SVMs will be briefly presented in this section. Readers can refer to
Vapnik (1998), Burges (1998) and Gunn (1998) for more details of SVM. For linear SVMs,
suppose the training data with n data points is denoted by ‫ ܦ‬as follows.
‫ܦ‬ൌ
௣
ሼሺ࢞௜ ǡ ‫ݕ‬௜ ሻȁ࢞௜ ‫ ܴ א‬ǡ ‫ݕ‬௜ ‫ א‬ሼെͳǡͳሽǡ ݅ ൌ ͳǡ ǥ ǡ ݊ሽ
Where ࢞௜ is a p-dimensional real vector (࢞௜ ൌ ሼ‫ݔ‬ଵ ǡ ǥ ǡ ‫ݔ‬௣ ሽ) and ‫ݕ‬௜ is a binary value indicating the
class that the point ࢞௜ belongs to.࢞ is also called the “features” of the SVM. The main task of
SVM is to find the maximum-margin hyperplane that clearly divides the two classes as much as
possible. A hyperplane can be defined by the following equation:
ࢃήࢄ൅ܾ ൌͲ
(1)
Where ‘ή’ denotes the dot product; X is the input vector (X={࢞ଵ ǡ ǥ ǡ ࢞௡ }); W is the vector
perpendicular to the hyperplane, and b is a constant. Figure 1 is a graphical representation for a
simple case with two-dimensional input (‫= ݌‬2) where the hyperplane is reduced to a linear
equation in the two-dimensional space.
According to this hyperplane, all the training data must satisfy the following constraints:
ࢃ ή ࢄ௜ ൅ ܾ ൒ ൅ͳ݂‫ݕ׊ݎ݋‬௜ ൌ ൅ͳ
(2)
ࢃ ή ࢄ௜ ൅ ܾ ൑ െͳ݂‫ݕ׊ݎ݋‬௜ ൌ െͳ
(3)
Which is equivalent to:
‫ݕ‬௜ ሺࢃ ή ࢄ௜ ൅ ܾሻ ൒ ͳ‫ ݅׊‬ൌ ͳǡ ǥ ǡ ݊
(4)
As shown in the above figure, the best hyperplane is the one that, not only separate the data
without error, but also maximizes the margin or the distance between the closest vectors in both
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classes to the hyperplane (Vapnik, 1995). In this paper, the delivery stops (y=1) and non-delivery
stops (y=-1) are separated with a p=3 (three features) linear SVM, as presented in Section 3.
Figure 1: Optimal separating hyperplane for a two-dimensional two-class problem
SVM for urban freight delivery stop identification
This section presents the SVM model for urban freight delivery stop identification. A case study
is then conducted for GPS data obtained from delivery tours to some grocery stores in the New
York metropolitan area. The stores are for a New York City-based chain of small supermarkets,
serving a mostly urban customers base. Most stores are within Manhattan including Roosevelt
Island except two stores in Brooklyn and Scarsdale. Daily log data, recorded by the delivery
vehicle drivers, for these tours are also available, which provide the “ground truth” delivery stops.
Table 1(a) and Table 1(b) provide respectively some sample GPS data and log data. The columns
of each table are self-explanatory. The latitudes and longitudes information from the GPS data
and the store number and name from the log data are purposely removed to protect the privacy of
the stores.
Table 1(a): Second-by-second GPS dataset
Index
Tag
Date
Time
Latitude N/S
Longitude E/W
Height
Speed
Heading
54
T
130227
205402
40.557885N
075.634331W
136
6
0
55
T
130227
205403
40.557866N
075.634321W
136
7
0
56
T
130227
205404
40.557844N
075.634306W
136
10
147
57
T
130227
205405
40.557819N
075.634290W
135
10
149
58
T
130227
205406
40.557793N
075.634273W
134
10
149
59
T
130227
205407
40.557765N
075.634259W
134
12
154
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Table 1(b): Driver’s delivery log
Store
#
1417
Date
2/27/2013
PC
PC
Store
name
Gristedes
Commit
time
Time in
Time out
Pieces
Weight
Time
at store
OK/Early
/Late
5p-11p
6:15 PM
7:00 PM
250
5017
0:45
ok
1422
0
Gristedes
5p-11p
7:30 PM
8:00 PM
307
5349
0:30
ok
1403
0
Gristedes
5p-11p
8:30 PM
9:00 PM
463
8513
0:30
ok
1445
0
Gristedes
5p-11p
9:30 PM
10:00 PM
278
5202
0:30
ok
(Note: some columns in table 2(a) and 2(b) are purposely removed due to the privacy agreements)
A three-stage approach is developed in this section using SVM to identify delivery stops from
GPS data. The first stage is to preprocess the daily log data to obtain all the ground truth delivery
stops and the second-by-second GPS data to find all the stops, including both delivery stops and
non-delivery stops. The second stage is to extract features from GPS data to determine which
combination of the features is the most effective to use in the SVM model. The last stage is to
implement the SVMs for urban freight delivery identification.
Preprocessing: ground truth from daily log and all stops from GPS data
The daily log is first processed to obtain the actual delivery stops. As shown in Table 1(b),
detailed information about delivery stops can be easily obtained from the daily log including the
store number, arrival time at each store, and departure time from each store, delivery time, and so
on. The results show that there are 42 delivery stops in total and that the minimum delivery time
is around 15 minutes. Notice that since the drivers always round the arrival or departure times to
the nearest 5 minute, the actual minimum delivery time could vary from 10 minutes to 20
minutes.
As with the second-by-second GPS data, as shown in Table 1(a), it records date and time,
latitude and longitude, and speed and heading direction. Date and time should be accurately
adjusted to the local time with consideration about daylight saving before further processing the
GPS data. Due to the urban canyon effect produced by tall building, long tunnels, etc. A speed
threshold of 14 kilometers/hour is used to detect vehicle stops in order to capture all potential
stops. Since this threshold speed is relatively high, if a delivery vehicle makes minor movements
around the stop location (e.g., move from one parking spot to another, circling to find a parking
spot, and so on), multiple vehicle stops may be identified. To resolve this issue, consecutive
stops less than 10 seconds apart are combined together as a single stop. This way, issues of idling
and minor movements around the stop can be instantly avoided. It was found that the difference
in terms of stop durations using speed threshold of 14 kilometers/hour and 0 kilometers/hour is
within 30 seconds, which is negligible to the minimum delivery time according to the daily log
(10 – 20 minutes). With the above method of data processing, there are 2249 stops in total
detected from the GPS data. Obviously most of the identified stops are non-delivery stops while
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only 42 of them are delivery stops according to the daily logs. This shows a considerable
imbalance in class distribution (i.e., 42 delivery stops vs. 2207 non-delivery stops).
Feature extraction for SVMs
The second stage is to extract features from GPS data for implementation of SVM to identify
delivery stops from non-delivery stops. A feature is a prominent characteristic of the data that
can be used to classify samples into multiple groups. From the daily log, it can be seen that a
delivery stop is relatively long and thus stop durations are first extracted as a feature in the SVM
model for delivery stop identification. The distributions of stop durations are shown in Figure 2.
Figure 2 shows that stop duration is an important feature for delivery stop identification.
However third person still cannot simply distinguish delivery stops from non-delivery stops
based on only stop durations, since the two types of stops (the squares and starts) clearly overlap
each other. Since a delivery stop is relatively long, it should result in relatively smaller average
speed for the entire stop duration. The average speed for the stop duration is thus examined as
another feature. The results are shown in Figure 3.
120
Non-delivery stop
Delivery stop
Stop duration (minutes)
100
80
60
40
20
0
0
500
1000
1500
2000
Stop ID number after data sorting based on stop type
2500
Figure 2: Stop duration of all stops
As shown in Figure 3, it seems that the average speed does contribute to delivery stop
identification but still there are some non-delivery stops mixed with delivery stops, even though
the percentage is not high. Based on the above figures, it is clear that the biggest challenge lies
in identifying long non-delivery stops from delivery stops. By showing all the long stops and all
delivery stops in Google Earth, all the delivery stops are concentrated in the New York City
while other long non-delivery stops are further away as shown in Figure 4 and Figure 5.
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14
Non-delivery stop
Delivery stop
Average speed of stop (miles/hour)
12
10
8
6
4
2
0
0
20
40
60
80
Stop duration (minutes)
100
120
Figure 3: Stop duration and average speed of all stops
Figure 4: Distribution of stops with duration over 500 seconds
Figure 5: Distribution of all the 42 delivery stops
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Therefore, the great-circle distance to the center of Manhattan is chosen as another feature. The
formulas for calculating the great-circle distance ݀ between two locations are given as follows:
݀ ൌ ‫ݎ‬ȟߪො
(9)
Here r is the earth radius (6,371,009 meters) and ȟߪො is the central angle between the two points
which can be calculated as below:
οߪො ൌ ƒ”…–ƒሺ
ටሺୡ୭ୱ థ೑ ୱ୧୬ ୼ఒሻమ ାሺୡ୭ୱ థೞ ୱ୧୬ థ೑ ିୱ୧୬ థೞ ୡ୭ୱ థ೑ ୡ୭ୱ ୼ఒሻమ
ୱ୧୬ థೞ ୱ୧୬ థ೑ ାୡ୭ୱ థೞ ୡ୭ୱ థ೑ ୡ୭ୱ ୼ఒ
ሻ
(10)
Here ‫׎‬௦ ǡ ߣ௦ ǡ ‫׎‬௙ ǡ ߣ௙ are the geographical latitudes and longitudes of the two points (s and f stans
for the two locations, such as the vehicle’s stop location and a toll booth), respectively;
ȟԄǡ ȟɉare their absolute differences. The data plotting are shown in the following figure.
180
Non-delivery stop
Delivery stop
Distance to the center of Manhattan (km)
160
140
120
100
80
60
40
20
0
0
20
40
60
80
Stop duration (minutes)
100
120
Figure 6: Stop duration and distance to the city center
Figure 6 shows that the stop duration and the distance to the center of Manhattan are two features
that are very effective to make reliable identification of delivery stops but there are still some
overlaps of the two types of stops (the squares and stars). It is highly possible that the number of
overlaps would be larger if the data sample is bigger so it is important to figure out how to deal
with this problem.
Since a vehicle may dwell at a major bottleneck (such as a toll booth, the entrance to a tunnel or
a bridge, etc.) in New York City during its journey for a long time, the distance to its closest
bottleneck is extracted as another feature. A binary value is set to imply a stop’s distance to the
closest bottleneck: the distance will be recorded as 1 if the great-circle distance is less than or
equal to 1000 meters or recorded as 0 otherwise. Here 1,000 meters are obtained by plotting
some of the tours again the major bottlenecks. This value is very rough and the SVM results are
not very sensitive to this actual threshold. Bottlenecks of all trips close to New York City can be
easily found with the help of Google Earth. The bottlenecks are detected, including the two ends
of Lincoln Tunnel and the toll booth in Weehawken (Latitude/Longitude: 40.76486/-74.023606).
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The data plotting of stop duration, the distance to the center of Manhattan, and the binary
distance to the closest bottlenecks is shown in Figure 7.
Figure 7 Stop duration, distance to the center of city, and distance to closest bottleneck
From Figure 7, it can be seen that the stop duration, the distance to the center of Manhattan, and
the stop’s binary distance to the closest bottleneck are three features that are the most effective to
identify delivery stops. In this paper, therefore, they are selected as the major features for the
proposed SVM model.
Implementation and results
As mentioned in section 3.1, there is a significant imbalance in the number of delivery stops and
non-delivery stops from GPS data. There are much more non-delivery stops than delivery stops:
2249 stops were identified from the GPS data while only 42 delivery stops were listed from the
daily log data. Such an imbalance in data will cause issues in SVM classification in general.
Many previous effects were made on classification data with imbalanced class distribution and
different approaches have been proposed, including the data-level approaches such as the
random sampling method (Chawla et al, 2004; Chawla, et al, 2002; Zhou and Liu, 2006), the
algorithm approaches such as applying different penalty terms for different classes in SVM (Lin
et al., 2002), the nested K-fold cross validation method (Alexander et al, 2005), and so on. In this
paper, a linear SVM with nested K-fold cross-validation will be implemented for delivery stop
identification. As concluded in Alexander et al (2005), the Nested K-fold cross-validation could
produce satisfactory estimation in the outer loop and model selection in the inner loop for
classification with imbalanced classes (see the pseudo code below).
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Figure 8: SVM with nested K-fold cross-validation (dashed box indicates the inner loop)
In this paper, third person implemented the linear SVM with nested 10-fold cross-validation,
( ൌ ͳͲሻ. Besides, m is also set as 10. The results are shown in Table 2. Notice that the
misclassification rate is defined as the ratio of the number of misclassified stops and the total
number of stops; false positive is defined as the number of non-delivery stops misclassified as
delivery stops; false negative is defined as the number of delivery stops misclassified as nondelivery stops.
Table 2: Results for delivery stops identification using GPS data
i
1
2
3
4
5
6
7
8
9
10
Error rate
0.0025
0.0022
0.0021
0.0021
0.002
0.002
0.002
0.002
0.002
0.002
Training dataset
False positive
False negative
2
1
2
1
2
1
2
1
1
1
2
1
2
1
2
1
2
1
2
1
Error rate
0.0024
0.0022
0.0021
0.0021
0.002
0.002
0.002
0.002
0.002
0.002
Testing dataset
False positive
False negative
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
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The above table shows that the linear SVM with nested K-fold cross-validation are highly
reliable and robust in delivery stop identification using second-by-second GPS data. The final
average performance is very satisfactory with an error of around 0.2% for both the training and
testing datasets. The number of false positive is 2 (most cases) and the number of false negative
is 1 for all training data; both false positive and false negative are 0 or 1 (only 1 case) for the
testing datasets.
The identified delivery stops can be used for stop analysis based on GPS data. For the case study,
the deliveries were both for regular hours (6am ~10pm) and off hours (10pm~6am) for four
grocery stores. After the delivery stops are identified, the average duration of delivery stops can
be computed as shown in table 3.
Table 3: Delivery stops analysis for four stores
Time period
All time periods
Regular-hour
Off-hour
NO. of
stops
Average duration
(minutes)
27
7
20
45.78
51.39
43.82
Standard Deviation
of stop duration
(minutes)
16.68
13.66
17.5
Maximum stop
duration(minutes)
Minimum stop
duration (minutes)
92.92
68.35
92.92
22.52
32.52
22.52
The above table shows that off-hour deliveries could help to reduce average delivery times at the
four stores although the standard deviation of off-hour delivery times is a little bit higher. Based
on the identified delivery stops, other freight performances such as fuel consumption and
emission can also be analyzed. The authors are working on this and results will be shown in
subsequent papers.
Discussions
In this section, third person briefly discuss some key issues related to the development of the
SVM method for freight delivery stop identification.
Preprocessing to avoid the problem of double parking or idling
A big problem of delivery stop identification is idling. To deal with this problem, a high speed
threshold (14 kilometers/hour) is first used to identify a stop. Two successive stops less than 10
seconds apart are then combined, which is easy to implement. It turns out that even with speed
threshold set as high as 14 kilometers/hour, the stop duration deviation is within 30 seconds
compared with 0 kilometer/hour, which is negligible to the actual delivery stop duration.
Furthermore, the delivery stop duration can be corrected or adjusted after the delivery stop
identification in the last step. It is obvious that the issue of minor movements at stores is also
solved by the preprocessing of GPS data. On the other hand, this method might not be suitable
for deliveries in extremely congested areas, resulting in double parking after circling around in a
large scale. This extreme case, however, barely exists in reality because delivery companies
always prefer to pay fines instead of circling for a long time to find a normal parking.
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Manual detection of major bottlenecks
The binary distance to a stop’s closest bottleneck is chosen as the third feature of the SVM
model to classify stops, which requires manual detection of major bottlenecks. To reduce the
manual efforts, only major bottlenecks that are on the delivery tours and close to the delivery
region, New York for the case study, should be inspected. Google Earth can be used to shown
the traces of second-by-second GPS data. Those bottlenecks could then be easily detected. Still,
this process requires some manual work which is the drawback of this algorithm, which,
however, can also guarantee the quality of the results.
Class imbalance problem
As aforementioned, the number of non-delivery stops is always much larger than the number of
delivery stops. A non-delivery stop could be caused by various reasons such as traffic lights,
congestion, tolling, waiting for ferry and so on, resulting in high frequency of non-delivery stops.
Most standard classification methods tend to produce better results for the majority class. In this
paper, the application of the nested K-fold cross-validation solved this problem. The results show
that the linear SVM with nested K-fold cross-validation is reliable in delivery stop identification.
Therefore the method is effective in addressing the issue of class imbalance problem.
Benefits of applying robust learning methods
The process of constructing SVM and the results in Section 3 highlight the importance of
applying robust learning methods, in this case the SVM method, for delivery stop identification
and for freight performance measurement using GPS data in general. Such learning methods are
highly adaptive and can learn key model parameters from the data. In essence, they learn from
the data the best combinations of the thresholds of the three features used in the SVM model.
Such thresholds might be manually or automatically determined via experimental studies, e.g., in
Greaves and Figliozzi (2008) and McCormack et al (2010). However those simple methods
suffer from at least two drawbacks. First, this requires significant efforts, sometimes manual for
trials and errors to determine the thresholds. Second and more critical, the methods can only
produce simple combinations of the thresholds: for example (for a two-feature SVM), “if feature
A is less than 100, and (or) feature B is more than 200, the stop is a delivery stop.” In reality, the
best combinations of the features could be more complicated, e.g., “If (i) feature A is between 0
and 100, and feature B is between 100 and 200; or (ii) feature A is between 100 and 200, and
feature B is more than 200, the stop is a delivery stop.” In this case, simple, intuitive methods
will have difficulty to determine those thresholds. Robust learning methods such as SVM
however can easily deal with those cases by learning both the structure and the actual thresholds.
They will be more beneficial to use in case the separating hyperplane is nonlinear, for which case
the simple intuitive method will obviously fail to work.
Notice that similar to the previous simple methods, some threshold values are also needed to
construct the SVM model. For example, during the preprocessing phase, 14 KPH is used
determine a stop and 10 seconds is used to combine consecutive stops. These threshold values
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however are straightforward and can be readily set by looking at the data. Moreover, they are
also needed by the previous simple, intuitive methods.
Conclusions and future research directions
In this paper, third person studied the feasibility of using SVM with nested K-fold crossvalidation to identify urban freight delivery stops using second-by-second GPS data. A threestage algorithm was developed including preprocessing of the GPS data, feature extraction, and
implementation of the SVM. The features that are extracted consist of stop duration, the stop’s
distance to the center of the city, and the stop’s binary distance to its closest bottleneck. To
resolve the class imbalance problem, nested K-fold cross-validation was used in the SVM
training process. A case study was conducted and the classification results based this three-stage
algorithm was highly reliable with an average error rate of around 0.2% for both the training and
testing datasets.
The three-stage SVM model proposed in this paper can be applied to urban freight delivery stop
identification, especially to deliveries that are made for small supermarkets or other deliveries
with relatively large stop duration and relatively concentrated delivery destination areas. Future
work should be conducted to test the SVM-based method on more urban freight GPS datasets to
further improve and validate the model.
The results from the SVM-based freight delivery stop identification can be used to analyze the
urban freight delivery performances such as mobility (speeds, travel times, delivery times, etc.)
and fuel consumption and emissions. The authors are working on this, particularly for the
purpose of evaluating the freight performances for off-hour deliveries (Holguín-Veras et al.,
2010). Results will be presented in subsequent papers.
The SVM model developed in this paper represents the first step of using robust learning
methods to explore large urban datasets (GPS data in this case) for urban freight performance
assessment. With the advent of urban BIGDATA, the authors believe that such a robust learning
based, data driven method may have great potential for urban freight performance measurement
and urban transportation modeling and management in general.
Reference
1. Alexander, S., Ioannis, T., Yerbolat, D., Constantin, F. A., 2005. GEMS: A system for
automated cancer diagnosis and biomarker discovery from microarray gene expression data.
International Journal of Medical Informatics 74, 491-503
2. Burges, C.J.C., 1998. A tutorial on support vector machines for pattern recognition. Data
Mining and Knowledge Discovery 2 (2), 121-167. Available from http://svm.first.gmd.de
3. Bishop, C.M., 2006. Pattern Recognition and Machine Learning, Springer.
4. Corinna, C., Vladdimir, V., 1995. Support-Vector Networks. Machine Learning 20, 273-297
5. Du J., Aultman-Hal,l L., 2007. Increasing the accuracy of trip rate information from passive
multi-day GPS travel datasets: Automatic trip end identification issues. Transportation
Research Part A, 41, 220-232.
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Original paper submittal - not revised by author
6. Richardson, E., Ban, X., Holguin-Veras, J., 2013. Identifying and Classifying Freight Trip
Stops from GPS Data. 92th Annual Meeting of Transportation Research Board, Washington,
D.C.
7. Gunn, S., 1998. Support vector machines for classification and regression. Technical Report,
Image Speech and Intelligent Systems Research Group, University of Southampton, UK.
http://www.isis.ecs.soton.ac.uk/resources/svminfo
8. Greaves, S. P., Figliozzi, M., 2008. Collecting Commercial Vehicle Tour Data with Passive
Global Positioning System and Technology. Transportation Research Record: Journal of the
Transportation Research Board, No. 2049, TRB, National Research Council, Washington,
D.C., pp. 158-166.
9. Hao, P., Ban, X., Bennett K., Ji, Q., Sun, Z., 2012. Signal timing estimation using sample
intersection travel times. IEEE Transactions on Intelligent Transportation Systems, 13(2),
792-804.
10. Holguin-Veras, J., Ozbay, K., Kornhauser, A., Shorris, A., Ukkusuri, S., 2010. Integrative
freight demand management in the New York city metropolitan area, Report submitted to the
United States Department of Transportation. Internet Link (Accessed on July 28, 2013):
http://transp.rpi.edu/~usdotp/OHD_FINAL_REPORT.pdf.
11. McCormack, E. D. et al., 2010. Developing a GPS-Based Truck Freight Performance
Measure Platform, WSDOT research report, pp.9-20
12. Chawla, N.V., Japkowicz, N., Kolcz, A., 2004. Editorial: special issue on learning from
imbalanced data sets. SIGKDD Explorations Special Issue on Learning from Imbalanced
Datasets 6 (1), pp.1-6.
13. Chawla, N., Bowyer, K., Hall L., Kegelmeyer, W.P., 2002. SMOTE: synthetic minority
over-sampling technique. Journal of Artificial Intelligence Research 16, 321-357.
14. Vapnik, V.N., 1995. The nature of statistical learning theory. Springer-Verlag, New York.
15. Sun, Y., Mohamed, K.S., Andrew, K.C. Wang, W., 2007. Cost-sensitive boosting for
classification of imbalanced data, Pattern Recognition 40, 3358-3378
16. Lin Y., Lee Y., Wahba, G., 2002. Support vector machines for classification in nonstandard
situations, Mach. Learn 46, 191-202.
17. Zhou, Z., Liu, X., 2006. Training cost-sensitive neural networks with methods addressing
the class imbalance problem. IEEE Trans. Knowledge and Data Engineering18 (1), 63-77.
18. Sun, Z., Ban, X., 2013. Vehicle Classification Using Mobile Traffic Sensors, Resubmittal to
Transportation Research Part C.
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