The effect of motorist information on commuter behavior: Classification of drivers into commuter groups

Transpn. Res.-C. Vol. I. No. Printed in Great Britain.

2. pp. 183-201.

1993

O96ExsOx/93 Q 1993 Pm&mm

$6.00 + .oO Press Ltd.

THE EFFECT OF MOTORIST INFORMATION ON COMMUTER BEHAVIOR: CLASSIFICATION OF DRIVERS INTO COMMUTER GROUPS L~VEDAY CONQUEST Center for Quantitative Science, University of Washington,

Seattle, Washington 98195,

U.S.A.

JAN SPYRIDAKIS and MARK HASELKORN Department of Technical Communication, University of Washington, Seattle, Washington 98195. U.S.A.

WOODROW BARFIELD Department of Industrial Engineering, University of Washington,

Seattle, Washington 98195,

U.S.A.

Abstract--This paper describes a methodology originally devised for analysis of travel-activity patterns and applies it to commuters’ responses to the influence of traftIc information upon commuting decisions. The method of cluster analysis was employed to identify commuter groups (from 3,893 motorists who responded to an on-road survey) with similar patterns of responses to the influence of t&Xc information. The resulting groups were defined as (a) route changers, willing to change route both on Interstate 5 and before leaving; (b) non-changers. unwilling to change departure time, route, or mode of transportation; (c) route and rime changers, willing to change route and departure time; and (d) pre-trip changers, willing to change departure time, route, or mode before departure but unwilling to change en route. Knowledge of such groups and their behavioral characteristics is useful in designing advanced traveler information systems that seek to affect commuter behavior and increase the efftciency of current transportation facilities. This paper discusses the methodology used to derive the commuter groups and investigates their commuting behaviors, decision-making processes, and information needs.

INTRODUCTION

The primary objective of the research described in this paper was to understand commuter’s behavior and responses to traffic information so that an Advanced Traveler Information System (ATIS) could be developed that would meet the needs of the commuting public. When studying commuter behavior, Mannering and Hamed (1990) have pointed out the importance of having richer sources of data, as opposed to more complex modeling approaches. Therefore, we did not attempt to build a mathematical model of route changes, departure time, or mode choice, as have other authors (e.g. Abu-Eisheh and Mannering, 1988; Alfa, 1989; Mannering, 1989; Mahmassani, Caplice and Walton, 1990; Mannering and Hamed, 1990; and Segal, 1979). Following Mannering and Hamed’s (1990) suggestions, we obtained a rich data set by conducting a survey of approximately 4,000 motorists in September 1988; this survey investigated commuter behavior and decision-making processes with respect to the receipt of traffic information, asking questions about motorists’ commutes, route choices, departure times, mode choices, use of and preferences for different types of traffic information, and demographic characteristics. The entire survey is displayed in Fig. 1; details on methodology underlying survey construction are contained in Spyridakis, Barfield, Conquest, Haselkom and I&son (1991). Since we did not expect all respondents to display the same behavior, we employed a market segmentation approach (Ben-Akiva and Lerman, 1985) to summarize 4,000 commuters’ responses to traffic information. Market segmentation involves partitioning an original (in our case, large) group into smaller, more homogeneous groups of individuals with interpretable major characteristics. This paper is concerned with the methodology and details of classifying the 4,000 commuters according to their responsiveness to traffic information. The resulting group classification itself was then used as a new categorical response variable in further statistical analysis, allowing us to compare these more homogeneous groups on various behavior patterns and demographic characteristics. This classification scheme and the subsequent analyses finally led IR(C) 1:2-F

183

Fig. 1. Questions from motorist informationsurvey.

Motorist

information

and commuter

behavior

185

to the development of design criteria for an ATIS prototype (Haselkom, Spyridakis, Barfield, Goble and Garner, 1991). METHODOLOGY

FOR CLUSTER

ANALYSIS

ON TRAVELER

DECISIONS

We chose the statistical method of cluster analysis to accomplish the segmentation of 4,000 motorists’ responses. Cluster analysis is a statistical technique that has been in use for over 30 years (Cormack, 1971; Anderberg, 1973; Eve&t, 1974; Ward, 1965). A variety of fields have found it useful for classifying subjects’ responses to many types of behavioral patterns-from people’s car buying habits, to psychological disorders, to medical diagnostic categories (Afifi and Clark, 1984). With respect to the design of information systems, Lewis (1991) advocated the use of cluster analysis as a market segmentation method in designing effective user interfaces for software systems. Cluster analysis has also been used by transportation researchers: Pas’s work (1982, 1983, 1988) on travel activity patterns contain a rich bibliography on uses of this method in transportation research. Cluster analysis uncovers an underlying structure in a data set by grouping cases or subjects into similar groups according to a specified distance metric, such as Euclidean distance. The objective of cluster analysis is to group a large number of cases into a smaller number of relatively homogeneous groups based upon similar responses on specifically chosen variables. Ideally, the data points within each cluster should be relatively close to the cluster center, and the cluster should be easily describable, thereby allowing interpretation and communication of cluster results. Because the ultimate goal of this project was the design of an ATIS to provide traffic information to commuters, we identified our major decisions that such an information system could affect: departure time, transportation mode, route choice prior to departure, and diversion to an alternate route while on a major interstate. Responses on these issues were elicited from the following survey questions, which offered responses of “never receive,” “rarely influenced, ” “sometimes influenced,” or “frequently influenced.” Questions C4, C5a, C5b, and C5c (outlined in Fig. 1) asked commuters to what extent traffic information influenced their en route changes, departure time changes, transportation mode changes, and pre-trip route changes. These were the specific variables chosen for the cluster analysis. The overall objective was to partition the entire group of subjects into mutually exclusive and exhaustive subgroups based upon similar patterns of responses to these questions. Cluster analysis is a valuable technique for grouping individuals into previously unknown groups. Many cluster programs allow the user to observe (from a single output) a “tree diagram” describing the sequence of cluster formation from which the user can choose an appropriate number of clusters by using summary information on distances between and within clusters at each step. The more clusters there are, the more alike are the individuals in each cluster; but this process can be carried to the extreme, where the number of clusters equals the number of individuals, with exactly one individual in each cluster. The other extreme is to agglomerate all individuals into a single grand cluster; a sensible grouping of subjects lies somewhere between these two extremes. These are known as “hierarchical techniques” because new, larger clusters are made by combining smaller ones-hence the hierarchical nature of the method. Johnson (1967) and Pas (1983) give more detailed descriptions of hierarchical techniques. However, all these techniques begin by considering each case as a cluster unto itself. In this study, the actual number of cases (3,893) was so large that it was computationally impossible to run a single hierarchical cluster analysis that would include all possible groupings from 1 to 3,893. We therefore used a non-hierarchical algorithm that set the number of clusters for a given run: The output for several choices for the number of clusters guided us to identify the number of clusters that had (a) neither too few nor too many members, (b) well separated cluster centers, and (c) easily interpretable major characteristics. For this analysis, we used the non-hierarchical SPSS program QUICK CLUSTER (SPSS, 1986), set up for efficient clustering of a large number of cases. The algorithm has three basic steps. For k clusters, the algorithm selects k cases with well separated, non-missing values as initial centers. Then the program assigns each case to the nearest cluster center (measured by squared Euclidean distance), and updates the cluster center. As the process continues, the centers tend to migrate toward concentrated observations. Finally, the algorithm reassigns each case to the nearest updated cluster center.

L. CONQUEST et al.

186

RESULTS

Before presenting the cluster centers for the selected cluster solution, it is highly informative to examine the final cluster results of five different possible solutions. The differences in the solutions relate to the final number of clusters; as Table 1 shows, we considered solutions from two to six clusters. In selecting the ideal solution, the goal was to choose the number of clusters that minimized the within-group sum of squares and the number of “splits”: For our purposes, “splits” were defined as being within a l/jy3 to a 50-50 breakdown between the “sometimes-frequently” and the “rarely-never receive” response categories. “Splits” on a variable represent more heterogeneity within a given cluster, and also cause difficulty in easily Table 1. Results of cluster partitionson 2-6 clusters

2

63.2 / 36.8

80.3fl9.7

10.9/89.1

85.2D4.7

Time change=, pre-trip mute changers, 63137 split on changes enroute (2,346).

33.2 / 66.8

10.9/89.1

Average cluster MS = 68721

3

Average ratio = 181058

5.5l94.5

10.V89.9 Time changem(543).

51.8J48.2

35.7164.3

l.lt98.9

63.3/36.7

80.7119.3

99.210.8

32.8167.2

98.61 1.4

Average error MS = .3993

68.7131.3

0.0/100.0

3.ol97.0

19.7180.2

1.3198.7

0.9i99.1

99.710.3 99.510.5

Average cluster MS = 332.76 50.9i49.1

89.2110.8

89.4110.6 47.3152.7

lOO.O/ 0.0 82.0118.0

25.9/74.1 0.9J99.1 97.512.5 14.3185.7 Average cluster MS = 25751 6

Average error MS = 3733

96. I/ 3.9

72.1/27.9 31.6168.4

5

17.3/82.7 Largely non-changershut 113will change enroute (1,260).

24.1n5.9

Average cluster MS = 297.05 4

0.8~99.2

39.6160.4

0.0/100.0

1.5/98.5 37.6162.4

90.1/ 9.9

O.lD9.9 7O.lR9.9 93.2/ 6.8

Average error MS = .2872 0.0/100.0

77.1/ 22.9

0.0/100.0

0.0/100.0

52/48 split& changes enroute, 36/64 split on departure time, 63/37 split on pre-tripmute changes (2425). ,kclknge~, including 33% on transport mode (638). Average ratio = 718.k

Route changers(744). Non-changers (844). Route and time changers (1,446). he-trip changers (572). including 38% on transport mode. Average ratio = 144252

k-trip route and time changers, 51/ 49 split on changes enroute. (1,875). 78.V21.8 lOO.O/o.O All-changers (142). 1OO.o/o.o 66.7/ 33.3 All-changers but split on changes enroute (47/53), pie-trip route. changers (67/33); (150). 0.0/100.0 1.4198.6 Non-changers (915). O.Of99.0 98.9fl.l Route changers (524). Average error MS = 2784 Average ratio = 936.63

Split (40/60) on enmute response; non-changers otherwise (424). 96.213.7 0.0/100.0 98.5/ 1.5 Route and time changers (1,020). 100.0/0.0 69.6130.4 0.0/100.0 3.4196.6 89.2/ 10.8 Route changers (740). 22.0/78.0 97.51 2.5 97.51 2.5 2.4197.6 Time and mode changers (41). 6.7193.2 64.6135.4 0.6199.4 30.2159.8 Split (65/35) on time response (1,187). 75.8/24.2 lOO.O/ 0.0 lOO.O/ 0.0 lW.O/ 0.0 All-changers (194). Average cluster MS = 247.02 Average ratio = 1112.78 Average error MS = .2215 able en&s are percentages of each group responding “Frequently” or “Sometimes” followed by percentages re-spmdmg “Rarely” or “Nevex Receive.” The four question content columns correspond to survey questions C4, CSa, CSb, C5c, respectively. Tbe number of cases in each group follows tbe commuter characteristics. Average (between) clustex mean square (MS) and average error MS am averaged over the four variables used in Le cluster analysis. Average ratio is the between clusters/within clusters ratio averaged over the four responses.

Motorist information and commuter behavior

187

describing the cluster. Therefore, the fewer “splits,” the better. Also, a lower within-group sum of squares implies more homogeneity in the cluster. Table 1 reveals that the lowest split arrangement occurred with the four-cluster solution. In addition, the behavior of the between- and within-cluster mean squares was such that the average ratio for the four-cluster solution was higher than all other ratios except the two-cluster solution (which contained two splits). With more than four clusters, the number of respondents in at least one of the groups dropped to a very low percentage of the entire data set. Fewer than four clusters was not as optimal in terms of the behavior of the between- and within-cluster mean squares. Hence, we concluded that a four-group-cluster solution represented a reasonable partition of the data set. The cluster analysis separated the 3,606 cases (those with no missing values on the cluster variables) into four major commuter groups. Table 2 reveals the final cluster centers selected by the SPSS algorithm for the four-cluster solution. The cluster centers represent a mathematical average of responses for members within each cluster and, as such, do not necessarily correspond to the actual responses, which have whole values of 0, 1, 2, or 3. Since each of the responses used to define the clusters has only four discrete possibilities, we tabulated the cluster results with respect to the individual variables, determined the major characteristics for that cluster, and ultimately labeled the cluster. Group 1 members are largely route changers. They are influenced by traffic information concerning route choice before and while they drive; however, the time they leave or their mode choice are rarely influenced. In contrast, Group 2 members seldom change their driving decisions based on traffic information. They are rarely influenced to divert to an alternate route, and are almost never influenced in the time they leave, and their mode and route choice. Group 3 members are time and route changers. Traffic information influences the time they leave, and their route choice before and while they drive. Group 4 members are willing to change the time they leave and their route choice if they have not yet departed. Traffic information influences their departure time and their route choice before they depart and sometimes their transportation mode. Distances between the final cluster centers are shown in Table 3. The centers are well separated; the smallest distance is 1.025, slightly more than one scoring point. Not surprisingly, an ordinary one-way analysis of variance by group on each of the defining variables shows large F values (Table 4). Assigning p values to these F values would be invalid, because the commuter groups were statistically created to maximize between-group separation and withingroup homogeneity. The F values can, however, be used as another indication of good cluster separation. Of the 3,606 cases, 20.6% (744) were assigned to the first group, 23.4% (844) to the second group, 40.1% (1,446) to the third group, and 15.9% (572) to the fourth group. Description of the commuter groups

From the results of the cluster analysis, we labeled the four commuter groups as follows (Bat-field, Haselkom, Spyridakis and Conquest, 1991). 1. Route Changers (RC) willing to change route both on I-5 and prior to leaving. 2. Non-Changers (NC) unwilling to change departure time, route, or transportation mode. 3. Route and Time Changers (RTC) willing to change both route and departure time. 4. Pre-trip Changers (PC) willing to make departure time, mode, or route changes before leaving home, but unwilling to change en route.

Table 2. Final cluster centers

Response Code: 3 = frequently; 2 = sometimes; 11 rarely; 0 = never receive.

L. CONQUESTet al.

188 Table 3. Euclidean

distance

between final cluster centers

Figure 2 shows the major cluster characteristics by displaying the percentage of each cluster responding “Sometimes” or “Frequently” for each variable used in the cluster analysis. The cluster groups were further investigated to uncover similar characteristics with respect to variables not used in the original cluster analysis. These groups often rearranged themselves in interesting ways, as discussed below. The commuter groups proved to be stable both over other variables not used in the original cluster analysis and through subsequent in-person interviews of group members chosen at random (Wenger, Spyridakis, Haselkom, Bat-field and Conquest, 1991). Alternative cluster combinations There are several categorical responses for which the clusters combined themselves into larger, meaningful groups. For example, we could set aside the NCs and combine the other three groups (RCs, RTCs, and PCs) and identify two groups that are easily labeled Nonchangers versus Changers. For each categorical response, a chi-square test for contingency table followed by the techniques of subdivision (Zar, 1984) allowed us to ascertain whether any combination into larger groups was possible. There were also seven particular responses on time or distance measures that were strictly continuous in nature. These came from questions A2, A3, and B5 on the survey (see Fig. 1). For Question A2 (entrances and exists from I-5), respondents filled in their usual entrance and exit ramps, and these were converted to distances later. Question A3 asked commuters to estimate their driving distances and times for their total commutes. Question 5B asked commuters how long they would wait on I-5 before diverting to either a known or unknown alternate route. Following the cluster analysis, the four commuter subgroups were compared using a one-way analysis of variance (Sokal and Rohlf, 1981). If the null hypothesis of equality of the subgroup means was rejected (as it was for all seven of the responses), further multiple comparisons of subgroup means were done to see which particular subgroups were statistically significantly from each other. The particular method used was that of Fisher’s Protected Least Significant Difference (Sokal and Rohlf, 1981); level of significance was set at .05. The following tables show characteristics of the four commuter groups; groups bracketed together are not significantly different from each other, either by the categorical response techniques or the continuous response techniques outlined above. Commute distances and time Table 5 shows southbound and northbound (to Seattle) freeway commuting distances in miles by commuter group, In both directions, the RC and PC groups travel shorter distances on the freeway than the NC and RTC groups. Commuters’ estimates of commuting distances and times are also displayed in Table 5, Route Changers appear to have the shortest estimated mean commuting distance (13.58 miles), whereas the other three groups average a longer mean distance of 15.46 miles. For time from both home-to-work and work-to-home, the Route Table 4. Analysis

Response En Route Change ‘Hi Change Mode Change Pre-route Change

Between Cluster MS 112.2 669.5 136.9 412.3

of variance results

DF (Between) 3 3 3 3

Within Cluster MS .35 .18 .30 .32

DF (Within)

F = Between/ Within

3.602 3,602 3,602 3,602

323.3 2712.6 459.4 1274.7

Motorist information and commuter behavior

189

69 Route Choice Before Departure

90

q

Departure Time

q

Diversion to Alternate Route

q

Transportation Mode

I

80

70 60 50

Route Choice Before Departure

eparture Time o Alterr PC

’ “P

Route

I

Commuter Group

Fig. 2. Percent of each clusteranswering “sometimes” of “frequently” to influence of traffic information on route, time, or mode decisions.

Changers exhibit the lowest mean time, the Non-changers the next lowest, and the Time Changers (RTC and PC groups) the highest mean time. For all groups, it generally takes commuters longer to get home than it does to get to work. Departure jlexibility and commute priorities An analysis of seven other commute factors-from departure flexibility to the importance of commute enjoyment-revealed other subgroups of the clusters (see Table 6). All groups gave similar response for their flexibility in departure time. Overall, all groups have more flexibility in time leaving work than in time leaving home. With regard to perceived stress during the commute, people who change the time they leave (RTC and PC groups, the Time Changers) experience more stress than those who do not (RC and NC groups). The Time Changers also care more in general about increasing both commute safety and commute enjoyment. The Table 5. Commuters’estimates of commuting distances and time (standarddeviations)

Time for Total Commute &I.inute@

I-5 Distance in Miles

RC PC NC

!TC

Southbound

Northbound

7.29 (6.28)I

(6.78)

7.72 (6.54)I

(6.72)

8.60 (8.90)

(921)

7.69

8.13 9.10

8.78

8.97

(7.56)

(7.41)

Total Commute Distance in Miles RC NC RTC PC

13.58

I

Hometo Work

(9.07)

28.96 (14.27)

work to Home 32.66 (15.46)

15.20

30.80

34.20

(10.30) I

(15.50)

(16.40)

15.71 ( 9.46)

32.57

36.46

(14.40)

(15.63)

15.23 (8.41)

I

32.48

36.22

(14.44)

(14.88)

Groups within vertical lines ate not significant Sfferent (p = 05) from each other using One-Way Analysis of Variance followed by multiple COI mptuiaonof means. RC = Route Changers;PC = Pm-trip Changers;NC = Non-changers:RTC = Route and Time Changers.

L. CONQUESTer al.

190

Table 6. Commuters’ responses to seven commuting factors (percent of each group)

ALot

SOIlE

Very Little

RC

14.7

47.2

38.1

R% PC

12.2 13.5 12.4

49.7 51.0 48.2

36.7 36.8 39.4

29.3

30.6 29.5 27.8

48.7 49.2 50.1 52.1

22.0 20.2 20.3 20.0

I

12.7 12.3

53.2 58.7

29.0 34.1 I

RTC PC

I16.9 16.7

60.3 60.8

23.0 I 22.3

RF: PC

67.8 69.6 66.6

28.8 26.6 30.5

03.3 03.8 02.9

NC

60.3

33.5

06.3

PC

22.4

40.5

37.1

RTC RC

I18.0 15.9

40.2 40.5

43.8 I 41.5

NC

14.0

34.7

51.2

I

59.1 57.4

35.5 34.7

05.4 I 08.0

I49.3 49.0

37.5 38.2

13.5 I 12.4

I

44.4 42.3

14.5 16.2 I

FaCt0l-s Flexibility in Depamre Time: Leave Home for Work

Flexibility inDeparture Time: Leave Work for Home RC

RE

PC

Amount of Stress Experienced During Commute %

Impomnce of Saving Commute Time

Importanceof Reducing Commute Distance

Importanceof Increasing Commute Safety RTC PC RC NC

Importanceof Increasing Commute Enjoyment RTC PC

41.4 41.5

33.9 42.9 23.2 29.8 44.7 25.5 . . . m.. . ... liroupswlmmveztxallmesamnots~gmtlcanuy auferaucp= ua)tramexn ornerusmguru-square anqsls of conungency RC

tables followed by subdivision. RC = Route Changers; NC = Nonchangus; trip changers.

RTC I Route and Time Changers; PC = Pre-

groups that constitute the Changers (RC, RTC, and PC groups) care more about saving com-

mute time than the Non-changers. The PCs place the most emphasis on reducing commute distance, followed by RCs and RTCs, and then NCs. As stated earlier, the PCs and RCs commute shorter distances than the RTCs and NCs. The concerns and pre-trip flexibility of the departure time changers make them prime candidates for home-delivered commuter information. Route choice The clusters were further evaluated for their familiarity with alternate route and factors that influence their route choices. The RC group is highest (73%) in terms of being very familiar with alternative routes to I-5, the other three groups averaging around 60% (see Table 7). If a commuter is going to change route in response to traffic information, then that person naturally needs to be familiar with available alternate routes. The RC group had the highest response, followed by the combined RTCPC groups in

Motorist information and commuter behavior

191

Table 7. Familiarity with north-southalternatemutes to I-5

Groupswithin ve&al lints arc not significantly diffaent (p = 05 frcaneach other using Chi-squareanalysis ofcontingencytabks followed by subdivision. RC = Route Changes NC = Nonchanm RTC = Route and Time Changers:K! = Pm-trip Chatlgus.

terms of frequently modifying or changing (a) the route from home to work and (b) the route from work to home (see Table 8). All groups modified the home-bound route more frequently than the work-bound route. As might be expected, the NC group was the least likely to modify either route. Table 8 also shows responses regarding the influence of the several factors on route choice: traffic reports and messages, actual congestion, time of day, weather conditions, and time pressures. The Non-changers respond less frequently than the Changers with respect to the influence of traffic reports and message, and the time of day, on route choice. Additionally, the RCs respond more frequently to traffic congestion with respect to route choice. All four groups are less likely to respond to time pressures or weather conditions. The NCs consistently respond the highest on “Rarely” and lowest on “Frequently” for all factors. The RTCs and the PCs, the ones who will change departure times, respond similarly to each other; similar percentages of each group will make route changes based on traffic reports and messages, traffic congestion, time of day, and, unlike the other two groups, time pressures. Table 9 displays the average length of freeway delay in minutes that members of each commuter groups would be willing to wait before diverting to an alternate route. When stopped by congestion, the RCs will wait the shortest average time (13.53 minutes) before changing to a known alternate route; the other three combined groups will wait a longer time of 17 minutes on average before diverting. A similar pattern exists for commuters’ likelihood to divert to unknown alternate routes: the NCs and PCs will wait the longest (combined average time, 27.4 minutes), and the RCs will wait the shortest time (22.13 minutes). These results are consistent with the route changing nature of commuters in the RC group; recall that they am most familiar with alternate routes (Table 7). Similarly, since the NCs prefer not to make any changes, and the PCs make decisions before leaving the residence, both groups are less likely to make changes en route. Receipt and use of trafic information When asked about their use and preferences for media delivery of traffic information, the RCs, RTCs, and PCs (the Changers) tend to exhibit similar characteristics (Table 10). The NCs usually have a higher percentage, responding that they had never received traffic information from a particular medium. At most, 35% of the Changers report ever receiving traffic information via television compared to an even lower percentage of the Non-changers. A little over half the Changers receive information from electronic message signs (VMS) over I-5, whereas less than half of the Non-changers never receive such information. Slightly less than half the Changers report receiving information from advisory radio (HAR) indicated by flashing lights on highway signs; only 35% of the Non-changers ever receive such information. In general, commuters who are less likely to modify a given behavior (Non-changers) are also less likely to receive information relevant to that behavior. For some responses, the Changers and Non-changers were more similar. From 789b-858 of all groups prefer commercial radio as the medium for traffic information either prior to leaving or while driving (or both); TV is the second choice prior to leaving home, and electronic message sign (VMS) is the second choice while driving. A more detailed interpretation of this information appears in Barfield et al. (1991).

L. CONQUESTet al.

192

Table 8. Frequencyof modifyingrouteand influenceof factorson route choice

Fkequcncyof Modifying Route from Home

to Work

Fkqtency of Modifying Route From Work to Home

RC

38.0

RTC PC

31.3 36.6

61.7 58.0 I

NC

03.4

20.1

76.5

RC

17.4

52.3

30.3

15.0 I 14.6

44.2 47.6

40.8 37.8 I

NC

10.8

31.6

57.6

EC PC

31.8 34.9 32.4

54.6 48.8 52.2

13.6 16.4 15.4

NC

09.2

39.7

51.1

RC

36.0

52.5

11.5

30.1 I 27.8

50.6 54.8

19.3 17.4 I

NC

18.1

45.0

36.9

RC RTC PC

23.2 26.5 30.6

41.3 39.3 37.7

35.4 34.2 31.7

NC

14.3

31.9

53.8

13.2 09.1 06.5 03.3

33.8 34.1 28.4 17.6

53.0 56.8 65.1 79.1

17.1 I 15.8

41.3 40.5

41.5 43.7 I

09.7 06.0

34.2 25.7

56.1 68.3

RTC PC

:

~t&~kaffic

Reports and Messages on

1nfluenceof Traffic Congestion on Route Choice

RTC PC

6nfluenceof Tiie of Day on Route Choice

&nfluenceof Weather Conditions on Route Choice

knfluenceof Time Ressures on Route Choice

EC RC NC PC RTC RC NC

Chys withinvertical linesarc not significantly different (p _.= 05) from t h other using Chi-square analysis of conthgency tables followedby subdivision.RC = Route Changers: R-l7 : Route and Time Changers: PC = R-e.tripCbangas;NC= Non-&angers.

Help from traflc information delivered by various sources. At the time of the survey, one-half to three-fourths of all groups claimed they never use TV to receive traffic information, yet there are clear differences between groups as to how helpful they would find televised traffic information (Table 11): 28% of PCs would find TV somewhat or very helpful as compared to 23% of RTCs, 14% of RCs, and only 7% of NCs. This ordering of groups (PC, RTC, RC, NC) finding various media somewhat or very helpful follows for almost all cases, with VMS (variable electronic message sign) ranging from 40% (PC) to 28% (NC); HAR (highway advisory radio) from 39% (PC) to 19% (NC); commercial radio ranging from 95% (PC) to 75% (NC); and highway construction phone hot line from 5% (PC) to 1% (NC). The PC, RTC, RC, and NC ordering constitutes a general reflection of commuter receptivity to commuter information; this would prove useful in designing advanced commuter information systems (Haselkom, Barfield, Spyridakis, Conquest, Dailey, Crosby, Goble and Garner, 1992). Preferred location for receiving traffic information ana’ choosing commuting route. The clusters also differ in preferences for where and when they want to receive traffic information (Table 12). As expected, the PC group has the highest response preferring to receive informa-

Motoristinformationand commuter behavior

193

Table 9. Length of freeway delay causing diversion to alternate routes (standarddeviations) Group

I

Known Route (mm.)

Unknown Route (mm.) 22.13 (13.25) 25.48 (15.41) 27.36 (17.42) I

Groupswithin verticallinea are not significantly different @ = OS: One-Way Analysis of Variance followed by multiple comparison of meana. RC = Route Changers;RTC = Route and Tie Changers;NC = Non-Changers; FC = pre-trip Changers.

tion before driving. PC commuters are also most likely of all the groups to choose their commuting route at home or work, and the least likely to make a change en route. The RTC group has the second highest percentage preferring to receive information before driving; a lower percentage actually choose the route at home or at work, whereas half choose the route on city streets or near entrance ramps. The majority of RC commuters are most likely to choose their commuting route on city streets or near entrance ramps. Almost half, however, prefer to receive traffic information at home or at work. About 10% of the NC group prefer to receive traffic information on I-5 (as opposed to a much smaller percentage for the other commuter groups), even though they are less likely to use the information than the other groups. Use of proposed up-to-the-minute traffic information. When asked whether they would use various proposed media that could deliver traffic information, most commuters answered “yes” to a dedicated ratio station (Table 13). Even the NC group, which tends not to actively seek traffic information, has a higher percentage (84%) responding that they would use traffic information from such a medium. The other three groups, which do actively seek information, respond with even greater affirmation to dedicated ratio. Other sources of traffic information Table 10. Media from which informationis received (percentage responding yes) ISCUOIUC

TV Before

Highway Advisory Radio

Phone

I 34.9 I

47.8

10.5

PC

35.3

46.2

08.5

RC

29.8

46.9

05.2

NC

16.6

46.3

35.2

04.0

Commercial Radio Only Before Driving

Commercial Radio Only While Driving

Commercial Radio Before and While Driving

PC

07.3 06.1

17.3 18.7

74.3 I 73.7

RC

05.9

25.1

67.7

Group

RTC

Driving

Message Sign WhileDriving

RTC

NC 1 04.2 40.6 49.6 Zroupswithin vertical lines ate not significantly different(p = 05) from each other using Chi-squareanalysis of

:ontingency tables followed by subdivision. RTC = Route and Time Changers;PC = Pretrip Changers;RC = Route &utgers; NC = Non-changers.

L. CONQUESTer al.

194

Table 11. Help from traffic information from various sources (percentage finding source somewhat very helpful)

Group PC

HAR

Comrnexial Radio

Highway Construction Phone Hot Line

39.6

38.8

94.9

05.6

TV 27.7

RTC

I 23.4 I

38.7

I 34.1 I

94.3

02.8

RC

13.9

34.6

26.6

94.3

01.6

01.3 NC 18.5 75.0 27.6 06.7 Groupswithin vertical lines are not significantly different@ = 05) from each other using Chi-squareanalysis of contingency tables followed by subdivision. FC = Pre-tripChangers; RTC = Route and Time Changers; RC = RouteChangers; NC = Non-changers. VMS = variableelectronic message sign; HAR = highway advisoryradio.

received a much lower response. For a proposed phone hot line, the three Changer groups range from 30%-42% responding yes; the NC group is lower with 23%. Traffic information delivered by computer appears even less desirable. For a proposed cable TV station dedicated to traffic information, the PC and RTC groups average 32% yes, followed by lower responses of 20% and 15% in the RC and NC groups, respectively. As computers and cable TV stations would currently have to be used before commuters leave home or work, it is not surprising that a larger number of those who are willing to make pre-trip changes (PCs and RTCs) like the idea of computer- or TV-delivered traffic information more than those who do not change (NCs) or make changes en route (RCs). Availability of information services and preference for development. All four groups are 84%-90% in favor of a dedicated ratio station (Table 14), with much lower responses for information via phone hot line, computer, or dedicated cable TV. Table 15 shows that around 90% of each group have radios available to them at home and in the car, or at home and in the office and car. Although 80% of each group have phones available at home and in the office, only a small percentage want to see telephone-hot-line traffic information services developed first (Table 14). Similarly, although a large percentage of all groups have TV at home (many have TV cable hook-up) (Table 15), less than 5% want to see a dedicated traffic station developed first (Table 14). Although 570/o-6% of all groups have computer facilities available Table 12. Preferredlocation for receiving traffic information and choosing commutingroute

Route is Chosen

Groups within vatical lines are not significantly different (p = 05) i?orn each other using Chi-squareanalysis of contingency tables followed by

subdivision. PC = ReArip Changers;RTC = Route and Tie = Non-changers;RC = Route Changers.

Changers; NC

Motorist information and commuter behavior

195

Table 13. Proposeduse of real-time traffk information(percentage responding yes)

Dedicated Group

RdiO

Phone Hot Line

Dedicated Computer

(Able TV

NC

84.4

23.0

11.7

15.5

RC

93.2

29.9

14.2

20.0

RTC

94.5

38.0

18.4

31.2

33.1 15.3 41.9 PC 95.9 Groupswitbin verticallines are not signifmantly different @ = 05) from each other using Chisquareanalysis of contingencytables followed by subdivision. NC = Non-Changers:RC = Route Changers;RTC= Route and Time Changers;FC = Rre-tripChangers.

at home, at the office, or both (Table 15), only l%-2% want to see computer-based traffic information services developed first (Table 14). A more detailed interpretation may be found in Bar-field et al., 1991). Demographics for the four commuter groups The demographics of the respondents (sex, age, income) vary by cluster (Table 16). Males

predominate in the RC and NC groups; females predominate in the RTC and PC groups. An analysis of variance by ranks rejected the null hypothesis of equality of mean age (rank) in the four groups, with the RC and NC groups showing the highest mean age rank and the RTC and PC groups having a lower mean age rank. Across groups, very few respondents (1% or less) were in the 65 + age bracket. As for income, an analysis of variance by ranks again confirmed that the RC and NC groups (older and more males) display the higher incomes, the RTC and PC groups (younger and more females) the lower incomes. The PC group, which is the only group that would change mode of transportation and has the highest percentage of female respondents, also displays the lowest income. Summary of commuter groups derived from combined clusters

As shown in the preceding discussion and tables, some clusters share certain characteristics and behavioral attributes. These shared aspects of the clusters allowed us to further group the commuters by actually combining specific clusters into different group patterns that contain more members with respect to categorical responses to the influence of and expressed needs for traffic information. By setting aside the Non-changers (NCs) and combining the other three groups (RCs, RTCs, and PCs), this yields two groups that were easily labeled Non-changers vs. Changers. There are several variables for which these groups separate themselves in this manner. In each case, a chi-square test followed by statistical subdivision (Zar, 1984) confirmed that the three Changer groups behaved similarly with respect to that particular response, whereas the Nonchangers behaved in a manner different from the Changers, Table 17 summarizes the responses that separate the Changers from the Non-changers. These 10 responses relate to reducing commute time and distance of choice of commuting routes, and perceived current and future usefulness of different types of traffic information. Table 14. Preferencesfor development of informationservices (percentageof each group)

Group %

Radio 90.5 I 88.7

Hot Line 06.1 06.4

Computer

Dedicated Cable TV

01.0 02.3

02.4 02.6 I

Groups within venial lines are not significantlydifferent @ = OS)born each other using Chi-square analysis of contingency tables followed by subdivision. RC = Route Changers:NC = NonChangers;RTC = Route and Time Changers;PC = Pre-tripChangers.

CONQUESTet al.

L.

1%

Table 15. Availability of informationsources

t3roup

None

Car Only

Home Only

OfEce Only

Homeand Car

Homeand oflice

H OffiF& ca;

Radio

%

NA

I02.2 03.1

02.7 05.3

01.7 01.4

03.5 05.3

:::

04.8 07.0 06.3 08.4

I 03.1 04.8 03.4 03.3

%

07.2 04.6 03.4 04.0

NA

82.8 85.0 84.6 83.4

37.0 37.4

NA

60.5

I I

29.5 34.2

29.2

I

35.8 32.4

NA

NA

48.7 50.1

02.0 I 02.6

40.9 I 42.5

42.1 44.1

02.8 04.2 I

49.7

NA

81.8 79.7 80.3 81.3

09.2 07.7

10.0 10.4 12.0 12.6

NA

NA

45.0

Phone

01.1 kE 01:2

TV Cable

Hook-up

60.5

FL’:

02.4 02.0 I

66.4 62.6

::?I

04.0 03.2 I

06.2

Computer 42.0

06.7 06.1

37.9 40.2

NA

NA

22.

El

NA

1:: !

PC I 33.0 06.6 40.6 19.8 Groupswithin vertical lines are not significantly different (p = 05) from each other using Chi-squareanalysis of contingency tables followed by subdivision. RC = Route Changers;NC = Non-changers:RTC = Route and Time Changers;PC = Rx-trip Changers.

Not surprisingly, Changers place more importance than Non-changers on reducing commute time and distance. Changers also claim that traffic reports and time of day affect their choices of commuting routes more than Non-changers. A higher percentage of Changers receive traffic information from electronic message signs and highway advisory radio while driving. Larger percentages in both groups prefer to receive traffic information from commercial radio before driving; the proportion was higher in the Changer group. There are statistically different percentages in terms of perceived helpfulness from freeway electronic messages and highway advisory radio, and much larger differences of perceived helpfulness from commercial radio. Both groups show large proportions claiming future use of traffic-dedicated radio, with the Changers exceeding the Non-changers by 10 percentage points. In general, the Changers claimed that they use certain types of available traffic information more than the Non-changers; a higher percentage also stated that they would use certain sources of traffic information if available in the future. Since the Changers made up over three-fourths of the sample, this is a good indication of a population willing to use the traffk information from a variety of sources and to change their commuter behavior accordingly. By combining the RTC and PC groups, we obtained a larger group that may be described by the label Time Changers, referring to people who change the time they leave their residence. Combining the other two groups (RCs and NCs) yields a group that may be labeled as Non-time Changers. Table 18 summarizes the responses that separate these two combined groups. Table 19 summarizes the responses that separate the Time Changers from the Route Changers and Non-changers, in the cases where the latter two groups do not combine.

Motorist informationand commuter behavior

197

Table 16. Commutergroup demographics

3Ollp RC NC RTC PC

RC NC

I

57.3 60.7

42.1 39.3I

I 20.6 21.9

41.7 38.4

24.2 26.2

12.7 11.6

0.8 1.8I

I42.1 46.4

57.9I 53.6

I25.9 28.9

35.4 35.6

24.3 33.2

09.9 14.1

1.4I 1.3

< 10

lo-20

20-30

I

04.5 05.7

12.2 11.6

Income (tiz&XlO’s) 30-40 50-60

15.6 16.0

16.9 15.2

60-75

18.5 17.6

75-100

17.5 17.6

14.0 15.3 I

RTC

PC 15.7 09.8 Groupswi inverticallines are not significantly different @ = 05) from each Other tig One-Way Analysis Of variance I ranks. RC = RouteChangers;NC = N~n-changers;RTC = Route and Tiie Changeax PC = pn-trip

chang=

Table 17. Summary of responses separatingchangers from non-changers(percentage of each group)

__--__-._--.

plXCMtiC

IInpoltance of Saving Commute Time:

Reducing Commute Distance:

CChoiceof Commuting Route Influence of Traffic ReporWMessages:

Influence of Time of Day:

Receipt of Traffic Inftnmation Fi-eewayElectronic Message Sign: Highway Advisory Radio: referencefor Trafk Information from ‘arious Sources Before Driving

telpfulnessof Freeway Electronic Message Sign:

commexialRadi0:

Pkdicted Use of Dedicated Radio Station ) Receive Traffic Information

2

KesDonse

Noncnangers

uumgns

Alot some very little

60.3 33.5 06.2

68.5 28.0 03.5

Alot

14.0

18.4

z&e

51.2 34.7

40.4 41.2

Frequendy sometimes MY

09.2 ;;::

33.5 51.0 15.4

Frequently Sometimes MlY

14.3 3:::

26.4 39.5 34.0

EtillYZg

46.3 53.7

56.1 43.9

Etil&lSg

35.2 64.8

47.2 52.8

TV Phone

79.4 12.6 07.3

;:: 03:1

Alot some very littWneverused

03.9 23.7 72.3

07.9 29.9 62.2

Alot some Very littlebver used

31.3 43.7 25.0

62.2 32.3 05.6

Yes No

84.4 15.6

94.5 5.5

R&-Ii0

L. CONQUESTet al.

198

Table 18. Summary of responses separatingtime changers from non-time changers

Responses

Commuting Factors

Time Changers 16.8 60.6

Non-time Changers 12.5 55.8

A lot Some Very little

57.8 z:;

49.2 37.9 13.0

Commute enjoyment

A lot Some Very little

41.4 42.9 15.7

31.7 43.8 24.5

Traffic infarmation received from phone

NolE Befo&whiledfiving

89.9 10.1

95.4 04.6

Preferencefor development of information sauces

Dediaedrdio Phone hot line Dedicatedcable TV Computer

85.9 08.3 Fl:!

89.6 06.3 02.5 01.7

Car only Home only Home/car HomJoffi Allthree

01.6 04.0 42.7 03.2 48.4

02.7

TV cable hook-up

None Home only Home/offke

30.9 65.3 03.8

37.2 60.5 02.2

Sex of wpondent

Male Female

45.2 54.8

59.1 40.9

Ageof respondent

< 31 3140 41-50 51-64 >64

28.0 35.5 24.0 11.1 01.3

21.3 40.0 25.2 12.1 01.3

Income of respondent ($l,CMk)

cl0 10-20 20-30 30-40 40-50

01.0 07.7 15.2 15.6 16.7

01.0 05.1 11.9 15.8 16.0

E >75

17.8 15.5 10.4

18.0 17.5 14.7

Slre.ssduringcommute

A lot

Some

Veq little

Importanceufincrasing commute safety

Availability of information sources

22.5

31.7

04.1 49.3 02.3 42.6

Time Changers (TCs) claimed to experience more stress during the commute than Nontime Changers @WCs) (Table 18). The TCs also place more importance on increasing both commute safety and commute enjoyment; they are concerned about the quality of their commute. They modify their home-to-work routes and their work-to-home routes less often than the RCs, but more often than the NCs (Table 19). Not surprisingly, TCs claimed that their route choices tend to be affected by time pressures more often than the RCs or NCs; traffic congestion affects their route choice much more than the NCs, but not as much as the RCs. A larger percentage of TCs tend to receive traffic information from TV (Table 19), commercial radio (Table 19). and phone (Table 18) than the NTCs. The TCs also claim to get more traffic information help than the RCs or NCs from TV and highway advisory radio (Table 19). A higher percentage of the TCs than RCs or NCs would use real-time traffic information from phone hot lines, computer, and dedicated cable TV (Table 19). The TCs exhibit similar preferences regardingfuture development of traffic information sources, with phone hot lines and

Motorist informationand commuter behavior

199

Table 19. Summaryof responses separatingtime changen from route changers and non-changers

CornmunngFactors

Responses

Frequencyof modifyingroutehome to work

Frequenuy Sometimes ft=tY

Time Changers 06.6 32.7 a.7

Frequencyof modifyingroutewah to home

Fresuendy Sometimes Rarely

14.9 45.1 40.0

17.4 52.3 30.3

10.8 31.6 57.6

Effectof timepressureon mutechoice

Frequently Sometimes -=lY

16.2 40.7 43.1

09.7 34.2 25.7

06.0 25.7 68.3

Effectof t&k congestionon mute choice

Fresuenuy Sometimes _lY

29.5 51.8 18.7

36.0 52.5 11.5

18.1 45.0 36.9

TmfticinformationreceivedfromTV

Yes No

35.1 64.9

29.8 70.2

16.6 83.4

Trafficinformationreceivedfromcommercialradio Beforedriving While driving Both

06.4 18.3 73.9

05.9 25.1 67.7

04.2 49.6 40.6

Traffii informationhelp fromTV

A lot/some Little/none

25.0 75.0

13.9 86.1

:::

Trafficinformationhelp fromHAR

A lot/some Little/none

35.4 64.6

26.6 73.4

18.5 81.5

Prqosed use, real-timetrafficinformation

Phone hot line (yes) complt= (yes) LkddedcableTV

39.1 17.5 31.7

29.9 14.2 20.0

23.0 11.7 15.5

Route Changers 07.5 38.0 54.5

Noachanters 03.4 20.1 76.5

dedicated cable TV at slightly higher percentages than the NTCs, and dedicated radio at a slightly lower percentage. A higher percentage of the TCs have a radio available at home, car, and office than the NTCs; and a higher percentage of the TCs have TV cable hook-up. Concerning demographics, a higher percentage of the TCs are female, compared to the NTCs with a higher percentage of males. The TCs also tend to be younger and have lower income than the NTCs. Setting aside the Route Changers (RCs) and combining the other three commuter groups yields a partition that may be labeled as Route Changers versus the Rest. Table 20 summarizes the responses that separate the RCs from the other three groups. The RCs’ estimated driving distance between home and work is shorter than the rest; RCs also express more familiarity with Table 20. Summary of responses separatingroute changers from the rest

Factor EstimateddrivingdistancebetweenhomearkIwcrk vay familiulwith altumate uorth-south routus: Somewhat familiar with ultcmute north-south mutes Not st all familiar with alteruste nath-south routes Length of freeway delay diverting to kuowu altemute mute Length of kcway delay divating to tmkuowu alteruate route Refer truffic iufonuution from commercial radio while dliving X = Route Changers; NC = Non-changers;

RC

NC, RTC, &IPC

13.58 mi. (SD = 9.07 mi.)

15.46 mi. SD = 9.52 mi.)

73.0% 24.3% 02.7% 13.53 min. (SD = 8.26 min.) 22.13 mitt. (SD = 13.25 min.) 84.5%

59.7% 35.0% 05.3% 17.03 min.

’ (SD = 11.26 min) 26.42 min. (SD = 15.% min.) 79.1%

RTC = Routean Time changers; PC = m-trip Changers.

L. CONQUESTet al.

200

routes than the other three groups. They wait the shortest amount of time in terms of freeway delay before diverting to a known or unknown alternate route; both groups wait about nine minutes longer before driving to an unknown alternate route, as compared to a known alternate route. Also, more RCs prefer to receive their traffic information from commercial radio while driving, as compared to other sources.

alternate north-south

SUMMARY The main objective of this particular research effort was to uncover patterns of commuters’ responses to traffic information as a required first step in developing an ATIS to meet the needs of various parts of the commuting population. Although our method of analysis was quite different from the model building approach taken by others (e.g., Mahmassani er al., 1990; Mannering, 1989; Mannering and Hamed, 1990), we also sought to confirm that there exist major segments of the commuting population that will display different types of commuting behavior. In particular, we were seeking groups that would respond differently to different types of trafIic information. This was done by taking a market segmentation approach, dividing up a large sample of 4,000 commuters into smaller, more homogeneous groups with easily describable characteristics in terms of how they would modify routes, departure times, and transportation modes in response to traffic information. We used cluster analysis as a segmentation technique for classifying the drivers into a small number of discrete categories according to four key questions in the survey. The cluster analysis did indeed allow us to identify four driver groups, each group showing major characteristics that results in assigning the labels of Route Changers (RCs), Non-changers (NCs), Route and Time Changers (RTCs), and Pm-trip Changers (PCs). To test the interpretability of the derived driver groups, we then compared the groups on various behavior patterns (using variables not included in the original cluster analysis) and demographic characteristics. We found that the clusters also recombined into larger groups in meaningful ways, based upon route changes, time changes, and other changes in general as a response to traffic information. The fact that “Changers” (RCs, RTC, and PCs) made up over 75% of the respondents is a good indication of a large fraction of a commuting population willing to use traffic information from a variety of sources and to change their commuting behavior accordingly. That slightly over half of the respondents (RTCs and PCs) have concerns about pre-trip flexibility and are willing to make departure time changes make them good candidates for more detailed commuter information delivered in the home, such as via computers or TV. Also, if morning traffic congestion could be alleviated by convincing certain segments of the commuting population to change the time they leave, this knowledge would be quite valuable to traffic planners. Finally, there was also a substantial segment of the respondents (NCs, 23.4%) who, despite the fact that they appreciate receiving traffic information largely via commercial radio, are far less likely to modify their commuting behaviors no matter how much traffic information they obtain. The four-group classification scheme, along with further information from followup interviews, finally led to the development of design criteria for an ATIS that would well serve commuters in the greater Seattle area (Haselkom, Barfield, Spyridakis, Conquest, Armstrong, Conigan, Grey, I&son, Loo, Piyarali, Scott and Wenger, 1990).

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