drivers' familiarity with urban route network layout in amman, jordan

Cities, Vol. 18, No. 2, pp. 93–101, 2001 2001 Elsevier Science Ltd. All rights reservedPergamon

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Drivers’ familiarity with urbanroute network layout in Amman,Jordan

Mohammad M Hamed and AA Abdul-HussainCivil Engineering Department, Jordan University of Science and Technology, P.O. Box 3030, Irbid, Jordan

Advanced traveler information systems (ATISs) have attracted a considerable amount of atten-tion in recent years. By providing drivers with traffic information, these information systemsare likely to help in reducing travel time, traffic-related congestion and consequently lead toreduced levels of vehicle emissions. With the presence of in-car navigation and communicationsystems, the ATIS can benefit commuters as well as unfamiliar drivers. However, in the absenceof in-car navigation systems – the case in most developing countries, familiar drivers are likelyto use these information systems more effectively and hence are likely to make well-informedroute-diversion and other travel-oriented decisions. This paper is devoted to developing driverfamiliarity models to identify the factors that influence drivers’ familiarity with the urbanroute network layout. A number of non-linear familiarity models are developed and coefficientsare estimated. Estimation results indicate that travel characteristics such as familiarity withone alternative route, residence leaving times, travel time to job location and the presence ofyoung children have a profound impact on the driver’s level of familiarity. Separate familiaritymodels for male and female drivers are also specified and estimated. When subjected to thesame set of predictors, the familiarity of male drivers turned out to be more responsive thanthe familiarity of female drivers. This result could reflect, among other things, the fact that maledrivers have more frequent and diverse activity stops that expose them to different sections ofthe transportation network layout. 2001 Elsevier Science Ltd. All rights reserved.

Keywords: Traveler information systems, Navigation, Urban routes

Introduction

Drivers’ familiarity with the urban route network lay-out is an essential component in route-diversion andguidance processes. Drivers who have higher levelsof network layout familiarity are likely to make well-informed decisions relating to route selection andother daily travel-oriented decisions (see Srinivasanet al, 1994; Bonsall, 1996). In addition, familiarity isan essential component for prediction of the driver’sroute-diversion propensity and information acqui-sition (Adler and McNally, 1994). Expected benefitsresulting from improved driver familiarity and effec-tive use of advanced traveler information systems(ATISs) include reduced traffic congestion and conse-

*Corresponding author. Fax:+962-6-4610793; e-mail: [email protected]

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quently reduced levels of vehicle emissions. More-over, familiarity with the urban route network reducesthe uncertainty associated with route diversion (Adlerand McNally, 1994). Trip and driver characteristics aswell as route attributes are the main determinants of thedriver’s familiarity with the route network layout.

Familiarity with the transportation network systemencompasses the knowledge of routes and links for-ming these routes (static knowledge of the urban routenetwork) as well as of the prevailing traffic and routeconditions (Lotan, 1997). Knowledge of routes andlinks forming them is based on the driver’s personalexperience which, in turn, influences the structure,design and implementation of ATIS. This paper dealsonly with drivers’ knowledge of the urban route net-work layout (static knowledge). The following sectionprovides a review of the empirical literature on issuesrelating to drivers’ familiarity.

Familiarity with urban route network: M M Hamed and A A Abdul-Hussain

Previous literature on drivers’ familiarity

Recently, Lotan (1997) addressed the influence of net-work familiarity on drivers’ route choice behavior.The study revealed that familiar and unfamiliar driv-ers exhibited different behavioral patterns thatresulted from differences in levels of network famili-arity. More specifically, unfamiliar drivers showed auniform distribution of alternative routes, while fam-iliar drivers showed distinct preferences among pro-vided alternatives. Adler and McNally (1994) alsoaddressed the issue of network familiarity and itsinfluence on drivers’ route choice behavior. Using ahypothetical network, the study reported that famili-arity has a significant influence on the driver’s per-formance. Drivers with higher levels of familiaritywere more likely to select efficient routes and wereable to react to perceived conditions and randomurban events. The study also indicated that driverswith lower levels of familiarity could benefit fromadvanced traveler information systems (ATISs).Abdel-Aty et al (1994) reported that drivers whoreceive en route or pre-trip traffic-related informationand have a high level of education were more likelyto use multiple routes to a job location. The studyalso revealed that traffic variations and poor trafficconditions on the usual route increased the propensityof route switching. These results implicitly indicatethat these drivers have higher levels of route networklayout familiarity.

Empirical results reported by Khattak et al (1995)indicate that drivers with knowledge of the route net-work layout were more likely to travel on alternativeroutes. Mannering et al (1994) reported that theflexibility of residence leaving times and the famili-arity with alternative routes decreased the delayrequired to induce route switching. Adler andMcNally (1994) reported a negative correlationbetween driving speed and the likelihood of routediversion. Pal (1998) addressed the factors that influ-ence the driver’s route-switching decisions. The studyincluded latent factors such as trust in traffic infor-mation systems, risk acceptance, and the expectationlevel of the quality of traffic-related information. Thestudy indicated that drivers with more trust in trafficinformation systems were more likely to switch toalternative routes to avoid traffic delay. The study alsoreported that drivers with a higher level of familiaritywith alternative routes have a higher propensity to usethese routes. Srinivasan et al (1994) studied the fac-tors influencing the driver’s driving performance.Factors addressed included in-vehicle route guidancesystem attributes, driver characteristics and trafficconditions. The study reported that driving perform-ance varied with experience and gender. Drivingexperience was positively correlated with the per-formance of the driving task in the presence oftraffic information.

Studies in the literature provide significant insightsinto drivers’ familiarity under the presence of traffic

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information systems. One key element of the ATIS,which has not been adequately dealt with in pastresearch, is the driver’s familiarity with the urbanroute network layout (static knowledge of trafficnetwork). In the presence of ATIS, increased driverfamiliarity helps drivers to maximize the use oftraffic-related information. In addition, identifying thefactors that are likely to impact the driver’s level offamiliarity may lead to a greater awareness and useof alternative routes.

The purpose of this study is to develop a method-ology capable of quantifying the driver’s familiaritywith the route network layout (the static knowledgeof route network layout). The paper will developmathematical models to investigate the factors affect-ing the driver’s familiarity. As will be shown, theanalysis results provide interesting insights into thisimportant problem. The scope of this study will belimited to drivers’ familiarity with the urban routenetwork layout during the pursuit of trips leading totheir job location. It is not the purpose of this paperto study the influence of driver familiarity on route-diversion behavior.

Drivers’ familiarity formulationThe dataTo estimate the coefficients of the driver familiaritymodels, data relating to drivers’ familiarity with theroute network layout during the process of commut-ing to work were collected through interview, in Juneof 1997. The data were collected from the northernpart of Amman, Jordan’s capital (see Abdul-Hussain,1998). This part of the city encompasses a large pub-lic hospital (University Hospital), Jordan University,major commercial centers, office blocks and a sig-nificant number of retail employment centers. Over900 drivers were approached at those locations. How-ever, only 498 drivers were willing to take part inthe survey.

The exact location of both residence and job wereidentified for each driver in the sample. In addition,each driver was asked about the specific route(s) usedto travel to their job location. He/she was asked torate his/her familiarity with the route layout leadingto the job location on a scale of 1 to 4 (4: veryfamiliar). Each driver was asked about the number ofroutes he/she was familiar with between his/her resi-dence and job location. They were asked to name(official or commonly used names) these routes. Fur-thermore, they were asked to rate their familiaritywith a map of Amman on a scale of 1 to 4 (1: poor).Although Amman maps are available, drivers or fam-ilies do not frequently use them.

Drivers were also asked to state the extent use ofen route traffic information (frequently, sometimes,rarely or never). Travel information that was availablein Amman at the time of the survey included semireal-time traffic reports through a special FM radioband and message signs. Other questions they were


asked included the number of years they had beenat their current job and residential address, distancetraveled daily to job location, and other questionsrelating to socioeconomic and demographic character-istics. Table 1 shows the sample summary of the inter-view statistics. It is noted that although the samplecontains a higher percentage of male drivers, most ofthe socioeconomic characteristics, such as age, num-ber of workers in household and number of cars inhousehold, and travel time to job location are fairlyreasonable and representative for commuters ingreater Amman.

Familiarity formulationHaving coded all information from the data collected,both residential and job locations were identified foreach driver on a paper map. The map contained majorand minor streets. The total number of available(reasonable) routes from residence to job locationwere identified for each driver. Sample descriptivestatistics showed that drivers who stated that theywere very familiar with other routes were not actuallyvery familiar. Fig. 1 shows the distribution of bothavailable and familiar routes. The figure clearly showsthat the majority of drivers who have alternativeroutes available are not aware of them. For example,68% and 28% of drivers indicate that they are familiarwith one and two routes, respectively. In fact, for themajority of drivers, there are two, three or four routesavailable to them. Fig. 2 shows the distribution of thenumber of years in residence and job locations. Fig.3 shows that about 10% of drivers surveyed are veryfamiliar with Amman’s map and 48% have poor fam-iliarity with the city map. Descriptive statistics showthat only 10% of sampled trips are carried out by car-pooling. Furthermore, 32% of drivers are familiarwith an alternative route. The sample average traveltime to job location was 18.35 min.

As such, we have two values for drivers’ famili-arity. The first relates to the stated driver familiarity(SF) taken directly from the survey. This familiarity(in rating form) was scaled as 1.00 for being veryfamiliar, 0.75 for somewhat familiar, 0.5 for little

Table 1 Sample summary of the interview statisticsa

Predictor Mean Percentage

Years at present residence 12.23Years at present job 6.80Total number of workers in residence 1.68Travel time to job location in minutes 18.35Use of en route traffic information (high/medium/little/poor) 5.42/20.68/20.28/53.62Level of familiarity with city map (high/medium/little/poor) 9.84/17.27/24.50/48.39Familiar with alternative route (yes/no) 26.91/73.09Number of workers in residence (1/2/3/4+) 45.82/33.81/12.17/8.2Number of cars in residence (1/2/3/4+) 50.10/34.20/11.12/4.58Gender (male/female) 72.5/27.5Marital status (married/single) 55.02/44.98Age (20–30/30–50/50+) 40.37/30.32/29.31

aNumber of observations=498

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familiar, and 0.25 for poor familiarity. The secondrelates to the computed familiarity (CF), which isdefined as the ratio of the number of familiar routes(indicated by the driver and taken from thequestionnaire) to total available routes (determinedfrom the paper map). The maximum number of avail-able routes (obtained from the paper map) was 4. Assuch four categories are created:

1. category one – represents one available route;2. category two – represents two available routes;3. category three – represents three available routes;

and4. category four – represents four available routes.

The computed weighted mean familiarity (CWMF)can be written as

CWMF =�CFini

N, (1)

where ni represents the total number of drivers in theith category, CFi is the computed familiarity, and Nis the total number of drivers sampled. Similarly, thestated weighted mean familiarity (SWMF) is writtenas

SWMF =�SFini

N, (2)

where SFi is the stated familiarity for the ith category.The final weighted familiarity for driver j (Faj) canthen be written as

Faj = CFj(RCWMF) + SFj(RSWMF), (3)

where RCWMF is the relative computed weightedmean familiarity and RSWMF is the relative statedweighted mean familiarity as shown below

RCWMF =CWMF

CWMF + SWMF(4)


Figure 1 Distribution of familiar and total available routes

Figure 2 Distribution of years at residence and job location

and

RSWMF =SWMF

CWMF + SWMF. (5)

Fig. 4 shows the distribution of the drivers’ finalweighted familiarity.

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Model specifications

Independent variables

It is postulated in this paper that the driver’s famili-arity with the route network layout (final weighteddriver familiarity) is a function of a wide range ofexplanatory variables (predictors). These predictorsrelate to prevailing traffic conditions (eg travel timeto job location), socioeconomic conditions (number


Figure 3 Distribution of drivers’ familiarity with city map

Figure 4 Distribution of drivers’ familiarity with route network layout

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of children in residence, ratio of cars to number ofworkers in residence), familiarity with alternativeroute(s), residence leaving times, number of years atboth residential and job locations, and prevailingtraffic information systems (eg en route trafficinformation). The dependent variable is the finalweighted driver familiarity as computed previously[Eq. (3)].

Functional formMathematically, the model that expresses the relation-ship between the driver’s familiarity variable andother explanatory variables (predictors) can bestated as

Fa = �(Z1, Z2, …, ZR, γ1, γ2, …, γL) + ε, (6)

where Fa is a vector representing the driver’s famili-arity values [Eq. (3)], � is a non-linear function ofthe R explanatory variables Z1, Z2, …, ZR and thecoefficients γ1, γ2, …, γL, and ε is the error termassumed to be normally distributed with variance δ2.The non-linear functional form is stated as

ψZ(Z, γ) = γ0 eγZ. (7)

Then, for a sample of N drivers, the likelihood func-tion can be written as

L(γ, Z, Fa) = (2πδ2)�N/2exp� (8)

�[Fa�γ0 eγZ]�[Fa�γ0 eγZ]

2δ2 �.

In addition, the log-likelihood function can be writ-ten as

ln L(γ, δ2, Z, Fa) = �N2

ln 2π�N2

ln δ2 (9)

�[Fa�γ0 eγZ]�[Fa�γ0 eγZ]

2δ2 .

Differentiating the log-likelihood function withrespect to the variance δ2 and solving for δ2 providesa maximum likelihood estimate for the variance (δ̄2)

δ̄2 =[Fa�γ0 eγZ]�[Fa�γ0 eγZ]

N. (10)

The maximum likelihood estimate for the variancecan then be inserted into the above log-likelihoodfunction to obtain a log-likelihood function in termsof the γ vector. The new log-likelihood function canbe stated as

ln L(γ, Fa, Z) = �N2

ln 2π (11)

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�N2

ln�[Fa�γ0 eγZ]�[Fa�γ0 eγZ]N ��

N2

.

Differentiating the new log-likelihood function withrespect to γ and setting the derivative to zero providesa maximum likelihood estimate for the γ vector.

Estimation resultsDrivers’ familiarity – pooled dataMaximum likelihood drivers’ familiarity estimationresults are reported in Table 2. Overall, the resultsreveal that the non-linear formulation of the drivers’familiarity is appropriate. The adjusted ρ-squaredstatistic (goodness-of-fit measure corrected for thenumber of parameters estimated) is 0.371 and the log-likelihood at convergence strongly rejects (with 95%confidence) the null hypothesis of no explanatorypower. In addition, each of the explanatory variableshas its expected sign and is statistically significantlydifferent from zero at the 1, 5 or 10% level (one-tailedtest). The general results clearly suggest a significantrelationship between drivers’ familiarity with theroute network layout and the predictors. A number ofpredictors were excluded from the final model owingto their statistical insignificance or due to multicollin-earity problems.

Table 2 clearly shows that the hypothesized vari-ables have a significant influence upon the driver’sfamiliarity. In particular, drivers who are familiar withone alternative route and receive en route traffic-related information have higher levels of familiaritywith the route network layout. Prevailing en routetraffic information is likely to motivate drivers toexplore different unused route(s) to get to their joblocation. In addition, exploring at least one route islikely to expose the driver to other unfamiliar routes.Previous empirical studies indicate that drivers whoreceive en route traffic information are more likely touse more than one route to their job location (seeAbdel-Aty et al, 1994; Mannering et al, 1994; Khat-tak et al, 1995; Pal, 1998).

The issue of multicollinearity prevented the use ofthe travel time indicator variable that represents theaverage travel time (18.35 min). The travel time indi-cator variable (1 if more than 30 min) was includedsince it was not highly correlated with other explana-tory variables. The negative sign on the travel time(more than 30 min) variable indicates that increasedtravel time (a surrogate for distance at least in ourcase – the city of Amman) is not likely to improvethe driver’s familiarity with the route network layout.Drivers with households located very far from theirjob locations are typically not familiar with the net-work layout surrounding their job locations. Theirlong daily commuting trips may discourage themfrom exploring other avenues to get to the joblocation. To be able to explore the network layout,they probably have to leave home very early. Such


Table 2 Drivers’ familiarity model estimation resultsa

Predictor Parameter estimate t-statistic

Constant 0.512 41.313***Familiar with one alternative route (1 if yes, 0 otherwise) 0.291 15.021***Use of en route traffic information (1 if poor, 0 otherwise) �0.073 �1.931**Travel time from residence to job location (1 if more than 30 min, 0

�0.104 �3.751***otherwise)Residence leaving time (1 if before 7:00 am, 0 otherwise) �0.121 �1.810**Ratio of years at job location to years at residence 0.007 1.859**Ratio of cars to total number of workersb in residence 0.032 3.721***Number of children between 6 and 17 years of age �0.016 �2.615**Number of children up to 5 years of age 0.020 2.101**

aLog-likelihood at convergence=�365.73; adjusted ρ-squared=0.371; *, significant at the 0.10 level, one-tailed test; **, significant at the 0.05 level,one-tailed test; ***, significant at the 0.01 level, one-tailed testbWith a driving license

early departure is not favorable due to “must do” earlymorning responsibilities. This result agrees with otherresults of earlier studies – that is, increased travel timeto a job location diminishes the effect of routechanges per month (see Abdel-Aty et al, 1994). Man-nering et al (1994) indicated that the longer the aver-age daily commuting time, the greater the traffic delayrequired to induce a route change.

Estimation results reported in Table 2 show that thetime drivers leave their residence reflects their levelof familiarity. Drivers with low levels of familiarityare likely to respond to daily traffic congestion byadjusting their residence leaving time (early leave),while drivers with higher levels of urban networkfamiliarity are likely to respond to traffic congestionby traveling on alternative routes. The interactionbetween leaving times and the propensity to use alter-nate routes has been addressed in the literature.Empirical studies have shown that drivers are likelyto switch routes (use alternative routes) and leavingtimes simultaneously. Khattak et al (1995) indicatedthat the availability of traffic information on usual aswell as alternative routes influences drivers’ leavingtimes to the job location.

It is also seen in Table 2 that the level of driverfamiliarity with the route network layout increases asthe ratio of the number of years at job location to thenumber of years at residence increases. The numberof years at job was normalized by the number of yearsat residence. The idea is to explore how instrumentalthe number of years at job is in shaping the driver’sfamiliarity. This result is consistent with the main-tained hypothesis that increased number of years atresidence/job location provides drivers with greatertime flexibility to explore different routes and henceincrease their personal experience with the route net-work layout.

The ratio of number of vehicles to the number ofworkers at residence appears to significantly influencethe driver’s familiarity with the network layout. Thisis an expected result and supports the notion that ahigher number of vehicles provides household mem-

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bers greater flexibility to use these vehicles at anytime by lowering the members’ dependency on onevehicle. Previous studies have shown that householdsize and the level of household vehicle ownershipmake significant contributions to variations in house-hold trip productions. Increased trip productions (tosatisfy various personal and/or residence needs)means expanded spatial distribution of trips. This islikely to expose the driver to new routes/links andhence increase his/her personal experience with theroute network layout.

Finally, Table 2 shows that drivers belonging tohouseholds with more young children (up to 5 yearsold) are likely to have an improved level of networkfamiliarity than those with older children. This resultseems to capture the special needs of young childrenand the diversity of places a household member has tovisit to satisfy young children’s needs. In other words,these drivers have an expanded spatial activity patterncompared with their childless counterparts. This pro-cess is likely to expose the driver to different placelocations and hence lead to a greater comprehensionof the route network layout.

Male/female drivers’ familiarity modelsThe driver’s gender turned out to be marginally sig-nificant (below the 10% level) and therefore was notincluded in the final model (Table 2). Although thecorrelation between the gender variable and thedependent variable was high, the correlation betweenthis variable and other independent variables reducedthe power of significance of the gender variable. Assuch, it was decided to assess the responsiveness ofmale and female drivers’ familiarity to the same setof predictors. Two separate non-linear models wereestimated. Table 3 shows the maximum likelihoodfamiliarity estimation results for both male and femaledrivers. The results clearly reveal that the familiarityof male and female drivers responds differently to acertain set of predictors. For example, number of chil-dren in residence, level of en route traffic information,residence leaving time, ratio of years at job to years


Table 3 Male/female drivers’ familiarity estimation resultsa

Parameter estimatePredictor

Male drivers Female drivers

0.610 0.632Constant(10.410)*** (17.321)***

Familiar with one alternative 0.2740.320 (9.143)***route (1 if yes, 0 otherwise) (12.721)***

Use of en route traffic�0.039 �0.041

information (1 if poor, 0 (9.750)*** (�1.231)otherwise)Travel time to job location (1

�0.093 �0.049if more than 30 min, 0 (�1.878)** (�1.283)**otherwise)Residence leaving time (1 if �0.162

0.173 (1.150)before 7:00 am, 0 otherwise) (10.721)***Ratio of years at job location �0.0010.011 (2.134)**to years at residence (�0.374)Ratio of cars to number of

0.034 (4.012)*** 0.030 (0.917)workers in residenceNumber of children between 6 �0.024

�0.021 (0.884)and 17 years of age (�2.113)**Number of children up to 5

0.020 (1.820)** 0.008 (0.823)years of age

at-statistics in parentheses; log-likelihood at convergence=�237.32and �100.50 for males and females, respectively; adjusted ρ-squared=0.369 and 0.283 for males and females, respectively; *, sig-nificant at the 0.10 level, one-tailed test; **, significant at the 0.05level, one-tailed test; ***, significant at the 0.01 level, one-tailed test

at residence, and the ratio of cars to number of work-ers in residence turned out to significantly (at the 1or 5% level) influence the familiarity of male driversonly. Other variables such as the travel time to joblocation and familiarity with one alternative route sig-nificantly (at the 1 or 5% level) influence the famili-arity of both male and female drivers.

It is interesting to note that the familiarity of maledrivers is highly influenced by the “ratio of cars tototal number of workers in household” variable. How-ever, the familiarity of female drivers is not influ-enced by this variable. Although the presence ofadditional vehicles in the household provides familymembers with greater flexibility to pursue more out-of-residence trips at different times of the day, femaledrivers seem not to benefit from such flexibility. Themost appealing theoretical reason is that female driv-ers are likely to be occupied with “in-residence”activities, particularly after returning from work. Assuch, female drivers are not well exposed (at leastin developing countries) to the physical layout of thetransportation network, hence the low level of famili-arity. Moreover, male drivers do more of the drivingthan female drivers at least in Amman. This includesdriving the children around.

Unlike its positive and significant influence on thelevel of familiarity of male drivers, the “ratio of num-ber of years at job to total number of years at resi-dence” variable has no statistical influence on thelevel of familiarity of female drivers. This result could

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be capturing some kind of habit formation on the partof female drivers and, as such, they become relativelyless enthusiastic to search for alternative routes. Insummary, the male/female driver’s familiarity resultsseem to indicate differences in their willingness toexperiment with different routes and/or differences inthe degrees of traffic frustration. In addition, thiscould be reflecting the fact that male drivers havemore frequent and diverse activity stops that exposethem to different parts of the route network layout.Previous studies have indicated that male drivers aremore likely to take alternative routes and have ahigher response rate to traffic-related information thanfemale drivers (see Mannering et al, 1994; Pal, 1998).

Concluding remarksIdentifying the factors that affect drivers’ familiaritywith the urban route network layout is a major build-ing block in successful advanced traveler informationsystems (ATISs). These systems provide drivers withessential traffic information about the prevailingtraffic conditions and alternative routes. This study isthe first to identify the factors that influence a driver’sfamiliarity with the urban route network layout. Sev-eral non-linear driver familiarity models aredeveloped and estimated. Empirical results revealedin this study complement past research results and arelikely to enhance the performance and efficiency oftraveler information systems.

Previous research has reported that, for ATISs tobe successful, it is important to know the type ofinformation desired by drivers (see Ng et al, 1995;Mannering et al, 1995). Empirical results from thispaper indicate the need to provide drivers with infor-mation relating to alternative routes. For example, ifa driver on a specific route is facing traffic congestion,due to traffic accidents or excessive demand, theATIS (through in-vehicle or a head-mile system)should provide appropriate information on alternativeroutes. Increasing the driver’s familiarity with alterna-tive routes increases the likelihood of using theseroutes. Adler and McNally (1994) reported that if thealternative route is familiar to the driver, there isincreased likelihood that a driver would considerdiverting on to that route.

In addition, drivers should be encouraged to max-imize the use of en route traffic information to assistthem in arriving at their final destinations. Ng etal.(1995) reported that private vehicle drivers ratedtraffic information as the most important feature anATIS can have. Furthermore, drivers should beencouraged to improve their familiarity with citymaps. A city map is considered an effective tool toincrease the knowledge of alternative routes. Driverscould be targeted at automobile clubs and the Depart-ment of Licensing.

Maximum likelihood estimation results indicatethat drivers’ socioeconomic characteristics, such asthe number of children (under 5 years old and


between 6 and 17 years) and the ratio of availablecars to total number of workers, had significant rolesin determining the driver’s familiarity. Empiricalresults also show that travel characteristics such asfamiliarity with one alternative route, residence leav-ing times and travel time to job location have a pro-found impact on the driver’s familiarity level. Driverswho receive en route traffic information and who arefamiliar with one alternative route are likely to havea higher level of route network layout familiarity thantheir counterparts.

Separate non-linear familiarity models for male andfemale drivers show that the familiarity of male driv-ers is more responsive to certain explanatory variablesthan the familiarity of female drivers. These resultsindicate that male drivers have higher levels of famili-arity with the route network layout than their femalecounterparts. These results could be supporting,among other things, the notion that male drivers havemore frequent and diverse activity stops (diverse spa-tial pattern movement) thus exposing them to differ-ent segments of the urban route network layout.Although male/female differences and the reasoningthat are stated in this paper might not be appropriatefor some developed countries, the developed method-ology can however be used with an appropriate dataset to estimate non-linear driver familiarity models.In terms of future research, further work is needed toestimate driver familiarity models that consider bothstatic and dynamic knowledge of the urban transpor-tation network and the route-diversion mechanism.

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