A STUDY ON THE INFLUENCE OF NETWORK ATTRIBUTESON THE ROUTE CHOICE BEHAVIOR
Sideney SCHREINERThe Regional Planning Laboratory
Tokyo University ofMarine Science and Technology
Koto-ku, Etchujima 2-1-6, Tokyo, Japansideney@gmail.com
Phone +81 3 5245 7366
Fax +81 3 5620 6492

Tetsuro HYODOThe Regional Planning Laboratory
Tokyo University ofMarine Science and Technology
Koto-ku, Etchujima 2-1-6, Tokyo, Japanhyodo@kaiyodai.ac.jp
Phone +81 3 5245 7386
FAX +81 3 5620 6492

Yoji TAKAHASHIThe Regional Planning Laboratory
Tokyo University ofMarine Science and Technology
Koto-ku, Etchujima 2-1-6, Tokyo, Japan
takaha@kaiyodai.ac.jp
Phone +81 3 5245 7366
Fax +81 3 5620 6492

Abstract: Route choice models are widely used for the prediction of passengers and cargo
movements by planners. This paper presents the application of a methodology, introduced
initially for the prediction of route choice of bicycles, now adapted to motorcycles and trucks.
The adaptation of the methodology allowed the improvement of the original analysis
algorithm increasing the number of considered network attributes and adding a user related
attribute: Value of Time. The case study of truck routes in Tokyo Metropolitan Area produced
satisfactory results relating the route choice behavior and the network attributes, especiallyindicating the relevant attributes to truck trips. As a result, the value of time for trucks inTokyo and the parameters relating route choice and network attributes were obtained.
Keywords: Route choice model, Route overlapping model, Network attributes, Value of time

1. INTRODUCTION
The route choice behavior of a pedestrian or vehicle driver can be analyzed by several
different methodologies which consider different input information and offer different levelsof results.
Ideally, computational algorithms that could be capable of selecting the exact path taken by
any traveler would provide the solution for route choice prediction. Realistically, algorithms
cannot perfectly predict the routes all travelers would choose, due the human complex
behavior.
Ramming (2002) comparatively analyzed the replication of paths generated by the mostcommonly used route choice models: C-Logit, Cross Nested Logit, Logit Kernel, IAP Logit,
and Path-Size Logit. In that analysis, one of the main concerns originated from the correctionof the overlapping among the route choice set.

The maximum route overlapping methodology avoids this problem by considering theshortest or, here, the cheapest calculated path instead of a set of possible paths. It is usuallypresented in a form that the actual route length or route cost is compared to the calculated
length or cost, and thus used as indicator of overlapping. Although this approach issatisfactory to most studies, it does not assess the level of duplication in the link usage.
Hyodo et al (2000) proposed a methodology based on the maximum route overlapping using aduplicate rate of link use as indicator of the model performance. That methodology eliminates
the issues related to the route choice set producing a single path as a result of both its iterative
process and the single combination of network attributes observed in real networks. In thatstudy, the model was applied to the route choice modeling of bicycles in two Japanese cities.
In this study, the methodology is improved aiming at its application to the route choicemodeling of motorcycle and trucks.
The main objective of this study is to evaluate the applicability of that methodology to othervehicles types and different network configurations.
2. THE MODEL: DUPLICATE RATE OF ROUTE OVERLAPPINGThe concept of the duplicate rate consists in defining the actual trip length of the n-th sample(Xn) as
X n = ∑ δ na × La

(1)

aWhere:δ na is a dummy variable (equal to 1 if the n-th sample passes the a-th link, or zero otherwise);
La is the length of the a-th link.
Hyodo et al (2000) proposed that users may perceive trip lengths differently according tocertain link attributes, suggesting the following relationship:
L*an ( β ) = La ∏ β kz ak(2)kWhere:L*an ( β ) is the perceived length of the a-th link at the n-th sample;
zak is the k-th attribute (e.g. road width) for the a-th link of the n-th sample;
β k is the unknown parameter for the k-th attribute.
Hence, the definition of the shortest path based on the perceived length takes the form
presented in (3).
X n* = ∑ δ * ( β ) na × LaaWhere:(3)δ * ( β ) na is equal to 1 if the n-th sample’s perceived shortest paths with parameter β include
the a-th link, otherwise it is equal to 0.
The duplicate rate D( β ) is then introduced as a measure to be fitted, defined asa∑ δ × δ (β ) ×L*nana

Dn ( β ) =a(4)XnRamming (2002) defines the replication of the real route in terms of total length or total cost,independent of which links are used by the real route and by the shortest path. Differently, theduplicate rate considers the total length of the coincident links (simultaneously in the realroute and in the perceived shortest path using attributes’ parameter β ).
The duplicate rate ranges from 0 (zero), when links of the shortest path and the links of thereal route are not coincident at all; to 1 (one), when the shortest path and the real route arecomposed by exactly the same links.
In this study, all the route samples will be used to obtain a general set of parameters thatmaximize the duplicate rate. Therefore, the weighted duplicate rate, which combines theduplicate rate of all the route samples, is defined in (5).

∑ ∑ δ × δ (β ) ×L
D(β ) =
∑Xnan*naaa(5)nn

The unknown parameters β are, then, arbitrated iteratively in order to maximize the duplicaterate. The maximized D( β ) is then named D-value.
2.1 From Shortest Path to Cheapest PathIn this study, the Route Overlapping methodology is applied to two case studies ofmotorcycles and trucks routes. Differently of the previous studies, which aimed at bicycles
routes, the motorcycles and truck drivers are assumed to give priority to the travel cost ratherthan travel length. Therefore, the main point in this study is the substitution of the perceived
shortest path by the perceived minimum cost path in the route overlapping definition.
The Generalized Cost (GC) represents the cost perceived by the user to travel through onelink. The form of the Generalized Cost for the a-th link is shown in (6).

GCa = (Consa + VOT × Timea ) × ∏ β kz ak(6)The calculation of the generalized cost can be broken in two components: the first,
considering the fuel consumption and the travel cost of the link; the second, considering theinfluence of the link’s attributes as in (2).
In the first component, the fuel consumption to travel through the a-th link (Consa) is
calculated considering the average engine performance in liters per kilometer traveled, thefuel cost in monetary units per liter, and the link length in kilometers. Also in the firstcomponent, the travel cost calculation considers the value of time (VOT) for the user and theestimated time to travel through the a-th link (Timea).
In the second component, certain link attributes are represented as dummy variables and theirrespective parameters in the same form as (2).
The introduction of the generalized cost incorporated an extra parameter in the model, whichis related to the users (drivers) rather than to the links, the Value of Time. The introduction ofthis parameter requires the modification of the general form of the model as shown in (7).

∑ ∑ δ × δ (β , ω ) ×LD( β , ω ) =
∑X
*nana

naa(7)

nn*
where δ na
( β , ω ) is equal to 1 if the n-th sample’s perceived minimum cost paths (for network
parameters β and value of time parameter ω ) include the a-th link, otherwise it is equal to 0.

3. ESTIMATION OF PARAMETERS
The process of estimation was based on the previous study, where the parameters for bicycles’
route choice were arbitrated within pre-defined limits. The parameters resulting in the highest
duplicate rate were, then, stored.
The previous study considered no more than three parameters simultaneously, and, using a
genetic algorithm application, up to 4 parameters. In this study, a new algorithm madepossible the simultaneous evaluation of up to 7 parameters.
The algorithm used here consists of the increasingly arbitration of the parameters, from alower pre-defined limit to a higher pre-defined limit, according to a defined increment.
This process, although simple, may require significant processing time depending on thenumber of parameters to be evaluated, the increment resolution (for example: 0,1 or 0,0001),
and the quantity of route samples. However, it provides a rather broad view of the influenceof each attribute on the route choice model.

4. MOTORCYCLE ROUTES IN HO CHI MINH CITY
The evaluation of the Maximum Route Overlapping methodology in Ho Chi Minh City(HCMC) was based on a sample of 167 motorcycle routes obtained in 2003, for the Study on
Urban Transport Master Plan and Feasibility Study in Ho Chi Minh Metropolitan Area(HOUTRANS).
HCMC was selected due to the high share of motorcycles in the urban traffic, around 90%,according to Matsuhashi et al (2005).

The network data, composed by 1819 nodes and 4866 links, as shown in Figure 1, provided
12 possible attributes for the links. From those, three attributes were selected initially to theapplication of the methodology: speed limit, road width, and pavement type. The otherattributes were considered to be interrelated. After the analyses, the pavement type wasremoved. It has not influenced the duplicate rate of route overlapping. Table 1 presents thethree criteria defined according to the selected attributes.
Two different analyses were performed: single parameter estimation and multi-parameterestimation. The first considered the variation of each parameter individually and its impacts
on the duplicate rate. The second considered the simultaneous variation of all parameters andtheir impact of the on the duplicate rate.

Figure 1 Network and route samples in Ho Chi Minh City
Table 1 Criteria of Attributes for Ho Chi Minh City
Attribute12speed limit
road width

Condition forβ na = 1> 40 km/h
> 3.0 mCondition forβ na = 0otherwise
otherwise

The results for the single parameter estimation are presented in Figure 2. The variation of theValue of Time showed no influence to the route choice behavior in Ho Chi Minh City. The
duplicate rate kept constant at 0.397252 for all the arbitrated parameters of value of time(from 0 to 100).

The results represented in Figure 2 indicate a higher influence of the attribute speed limit overthe other attributes. Similar to the Value of Time, the attribute pavement type have notinfluenced the duplicate rate.
individual influence (vot=1)
0.41
0.4

Overlapping

0.39
0.38
0.37
0.36
0.35
0.34
0.33
0.5

0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

dummy parameter
speed limit > 40km/h

lane width > 3.00m

pavement

Figure 2 Individual estimation of parameters for Ho Chi Minh City
The simultaneous variation of all parameters can indicate how the parameters relate to eachother to influence the duplicate rate of overlapping route. In the case study 1, as shown inFigure 3, the maximum duplicate rate is not influenced by the combination of the attributes.

5. TRUCK ROUTES IN TOKYO
The sample for Tokyo represents 597 routes of trucks, in a network composed by 9231 nodes
and 25062 links, as shown in Figure 4.

Combined influence of the dummy parameters (VOT=1)
0.41
0.4

Overlapping

0.39
0.38
0.37
0.36
0.35
0.34
0.33
1

2141

61

81

101

121141

161

181

201

Figure 3 Simultaneous estimation of parameters for Ho Chi Minh City

Figure 4 Network and route samples in TokyoThe attributes of the links were selected considering the results of Matsuhashi (2005), wherethe parameters of the link’s attributes available in the route sample were evaluatedindividually in order to identify the most significant conditions. The attributes used toestimate the duplicate rate are presented in Table 2.

123456Table 2 Criteria of Attributes for TokyoCondition forCondition for
Attribute
β na = 1β na = 0
Tolled roadtolled linkotherwise
CBD area
inside CBD areaotherwise
Ring Road 7
inside ring road7
otherwise
Number of lanes= 4 lanes
otherwise
Heavy truck permission
yesotherwise
Tall truck permission
yesotherwise

The attributes 5 and 6 refer to the restrictions to weight and height in the roads within theTokyo Metropolitan Area. Those attributes are directly related to the possible trucks’ routes,
therefore expected to have significant influence in the route choice model. Figures 5 and 6
show the links where heavy trucks and tall trucks are permitted, respectively.

Figure 5 Heavy trucks permitted links in Tokyo
Similar to the parameter estimation for HCMC, the procedure was performed considering theindividual and simultaneous variation of the parameters.
The first aspect to be considered in the route overlapping estimation is the variation of thevalue of time. Prior studies have pointed to an average VOT between JPY 50 and JPY 200 per
minute, depending on the vehicle type. These values are used in this study as the limits for thevalue of time variation. All other dummy variables were kept constant (equal to 1) for thisestimation.
The estimation of the network related parameters must be related to the value of time
estimation since this presents a strong influence on the maximum duplicate rate. The results
for the estimation of each individual parameter in association to the Value of Time are shownin Table 3.

Figure 6 Tall trucks permitted links in Tokyo

The simultaneous estimation of the parameters showed a strong interrelation among theattributes, reflected in the maximum duplicate rate achieved. As Table 4 shows, the duplicate
rate obtained by the combination of the attributes was higher than the obtained by theindividual attributes.
The sample data set included the classification of the vehicles in two types. The two types
correspond to container trailers and other trucks. Considering that container trailers have more
restrictions to urban traffic than other types of trucks, the two types were separated and themulti-parameter estimation was once more conducted. The results indicate that significantly
higher duplicate rates can be obtained by the appropriate characterization of the sampledvehicles. Table 5 and Table 6 show the results for the container trailers and for the other typesof trucks, respectively.
The results for the container trailers indicate a significantly lower value of time for this typeof vehicle. Moreover, the parameters indicate that routes chosen by container trailers are
influenced mainly by the roads’ restrictions of weight and height.
The results for the other types of trucks, Table 6, indicate influence from the other attributes
such as tolled roads and number of lanes.

Tokyo0.62
0.6Overlapping

0.58
0.56
Max Overlapping (VOT=111) = 0.610

0.54
0.52
0.5

VOT
Overlapping

Figure 7 Estimation of Value of Time (JPY/min) for Tokyo

Table 3 Individual Parameters for Trucks in Tokyo
AttributeVOTTolled roadVOTCBDVOTRing Road 7VOT4 lanes
VOTWeight
VOT
HeightParameter
73
0.710
98
0.990
98
0.995
106
0.785
75
0.600
950.600

Duplicate rate
0.632959
0.607072
0.607312
0.642533
0.650342
0.645822

Note: Value of Time (VOT) in JPY/min

Table 4 Combined Parameters for Trucks in Tokyo
Attribute
VOT
Tolled road
CBD
Ring Road 7
4 lanes
WeightHeightNote: Value of Time (VOT) in JPY/min

Parameter
97
0.9
0.8
1.0
0.9
0.8
0.6

Duplicate rate

0.670913

200

194

188

182

176

170

164

158

152

146

140

134

128

122

116

110

98

104

92

86

80

74

68

62

56

500.48

Table 5 Combined Parameters for Container Trailers in Tokyo
Attribute
VOT
TolledCBD
RingRoad7
4 Lanes
WeightHeightParameter
141.01.01.0
1.00.80.9

Duplicate rate

0.669285

Note: Value of Time (VOT) in JPY/min

Table 6 Parameters for Non-Container Trailer Trucks in Tokyo
Attribute
VOT
TolledCBD
RingRoad7
4 Lanes
WeightHeightParameter
900.6
1.0
0.9
0.6
0.80.7

Duplicate rate

0.803336

Note: Value of Time (VOT) in JPY/min

6. CONCLUSIONS
The main conclusions of this study are related to the applicability of the maximum route
overlapping methodology.
The adaptation of the methodology to consider the perceived route cost rather than theperceived route length was successful. As a result of this adaptation, an alternative process forthe estimation of the value of time was presented. This process is especially interesting forindicating that value of time is an influential parameter to the route choice model only in thepresence of tolled roads (which represent the expressway system).
The utilization of different increment resolutions (e.g. 0.1 or 0.001) implies in refinement of
the maximum duplicate rate, which will be subject to a sensibility analysis to identify thelevel of impact in an all-or-nothing assignment in the next steps of this research.
The results of the analysis of motorcycles routes in Ho Chi Minh City indicate that some
attributes do not interact when influencing the route choice behavior, specifically in the caseof motorcycles. Further researches should attempt to identify which attributes significantly
influence the route choice behavior of motorcycles.
The analysis of the truck routes in Tokyo indicate that truck route choice behavior issignificantly influenced by several attributes, mainly for those representing the limitations
imposed by the network to the larger sizes of trucks. Moreover, the maximum route
overlapping methodology has successful estimated the value of time for the truck routes. Also,the results of this case study suggest that route choice behavior for freight transportation may
be influenced by the type of trucks. These results also suggest that an assignment procedurethat considers vehicle type based route choice models are sound and coherent, and, therefore,
will be considered in the next steps of this research.
The iterative nature of this methodology requires the improvement of the shortest path searchalgorithm in order to reduce computation time and possibly permit the introduction of ahigher number of attributes and higher increment definition.

ACKNOWLEDGEMENT
The authors are especially thankful to the Tokyo Metropolitan Association of Transportation
Planning for the provision of truck route data and the road network data.

REFERENCES
Hyodo, T., N. Suzuki and K. Takahashi. (2000) Modeling of bicycle route and destination
choice behavior for bicycle road network plan. In Transportation Research Record:
Journal of the Transportation Research Board, No 1705, TRB, National ResearchCouncil, Washington, D.C., pp. 70-76.
Matsuhashi, N. (2005) A Study on the Qualitative Route Choice of the Large-Sized
Freight Trucks in Tokyo Metropolitan Region. M.Eng. Thesis. Tokyo University ofMarine Science and Technology, Tokyo. (In Japanese)
Matsuhashi, N., T. Hyodo and Y. Takahashi. (2005) Image Processing Analysis ofMotorcycle Oriented Mixed Traffic Flow in Vietnam. In Proceedings of Eastern Asia
Society for Transportation Studies, Bangkok, pp. 929-944.
Ramming, M. (2000) Network Knowledge and Route Choice. Ph.D. Thesis. Massachusetts
Institute of Technology.