Effects of violent crime and vehicular crashes on active mode choice decisions in New York City

Travel Behaviour and Society 18 (2020) 37–45

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Effects of violent crime and vehicular crashes on active mode choice decisions in New York City Nicholas S. Caros1, Joseph Y.J. Chow

T

⁎,2

C2SMART University Transportation Center, Dept of Civil & Urban Engineering, New York University, 15 MetroTech Center, Brooklyn, NY 11201, USA

ARTICLE INFO

ABSTRACT

Keywords: Active transportation Crime Mixed logit Travel behavior New York City

Substantial research has explored the effects of different variables on mode choice with the intent of understanding this behavior so that active modes can be encouraged. This study furthers that effort by investigating the impact of perceived danger from crime on the probability of choosing an active mode of transportation among travellers in New York City from 2009 to 2011. This study uses trip data from the New York Metropolitan Transportation Council Regional Household Travel Survey along with historical crime and vehicle collision data to estimate a random utility model. Traveller demographic information, travel cost and incidence of crime and vehicle collisions involving pedestrians or cyclists are used as explanatory variables in a mixed logit model. The finding implies that travellers could be encouraged to cycle by reducing crime levels, or by being provided an alternative route with less crime. Based on the model results it can be determined that travellers are willing to pay $0.66 for a 1000-point reduction in crime severity. An increase in crime of 1% has a much greater impact on bike ridership (2.11% reduction) than on walking (0.06% reduction). Removing crime completely would improve a traveler’s trip satisfaction by as much as $0.26 per trip. Compared to crime, collision rate has a much stronger impact on bike ridership, with an elasticity of −7.56 (3.6 times higher elasticity than crime).

1. Introduction

As will be shown in Section 2, no study has yet been conducted on the effect of crime and traffic collisions on travel mode choice using revealed preference data. Furthermore, existing studies have not accounted for heterogeneity in taste preferences of the population when considering both vehicular crashes and violent crime. This study addresses both research gaps with a mixed logit model of active mode choice with violent crime and vehicular crash concerns estimated from revealed preference data in New York City.

Active mode choice (walking and cycling) is a benefit not only to the traveler but to society as a whole. Health outcomes are much better for those who use active modes (see Mueller et al., 2015). Getting travelers out of their cars and onto their feet or bicycles also reduces carbon emissions, improves air quality and lowers congestion on city streets. For drivers, personal safety is likely not a significant concern when planning a route as they are relatively secure in their vehicle. Pedestrians and cyclists on the other hand may be more inclined to avoid areas they perceive to be dangerous or choose to take a taxi or bus instead. By studying how people choose to travel and comparing that to areas of high crime and traffic incidents we can see whether these factors affect their decisions on whether or not to take a car, transit, bicycle or walk. If travelers are making decisions based on personal safety concerns, then providing them information about the relative safety of available routes would encourage them to choose an active mode. Having this information easily available to the public might also incentivize local governments to target problem areas with safer street design or more police patrols.

2. Literature review Recent research in regards to safety affecting travel decisions focuses primarily on route choice modelling rather than mode choice. Segadilha and Sanches (2014) identified factors that influence cyclists’ route choice using a questionnaire and found that traffic volume, speed and composition are of primary concern, although security and street lighting are also important. Similarly, Hodgson et al. (2004) reported a wide variety of factors that affect pedestrians’ route choice including personal security and perception of danger based on a survey. The potential for using informatics to inform travelers about safety

Corresponding author. E-mail addresses: [email protected] (N.S. Caros), [email protected] (J.Y.J. Chow). 1 ORCID: 0000-0002-1060-6781. 2 ORCID: 0000-0002-6471-3419. ⁎

https://doi.org/10.1016/j.tbs.2019.09.004 Received 29 October 2018; Received in revised form 23 September 2019; Accepted 25 September 2019 2214-367X/ © 2019 Hong Kong Society for Transportation Studies. Published by Elsevier Ltd. All rights reserved.

Travel Behaviour and Society 18 (2020) 37–45

N.S. Caros and J.Y.J. Chow

conditions was studied by Yannis et al. (2008) although it included only drivers. The authors developed a discrete choice model for route choice and argued that providing route safety information to drivers via advanced traveller information systems would improve road safety. Active mode choice decision making has been of interest to researchers for some time. The route and traveler characteristics that influence bicycle mode choices have been explored by Handy and Xing (2011) and Winters et al. (2011) in the U.S.A. and Australia, although traveler surveys were used for both studies. Adkins et al. (2012) used a regression model to study the aspects of urban design that promote or deter walking in urban areas. Research in modelling the perception of safety in mode choice decisions is typically limited to safety from vehicle collisions and does not focus on crime. One such study of the impact of cycling infrastructure and built environment was conducted by Aziz et al. (2017). The authors estimated a mixed logit model using the New York Metropolitan Transportation Council (NYMTC) Regional Household Travel Survey data to evaluate the impact of different demographic attributes and physical characteristics of the travelled route on mode choice. Their model included land use and infrastructure information but did not include the impact of crime on active mode choice decisions. A paper by Kamargianni and Polydoropoulou (2013) used a latent choice model to study whether the built environment affected the mode choice of teenagers when travelling to school. Some recent literature has explored the relationship between crime and active mode choices. Fyhri et al. (2010) conducted a survey of literature to identify risks that are important to pedestrians and found that fear of crime and crashes are both important factors in mode choice. Their review focused only on pedestrian behavior and the empirical components included stated preferences, not revealed preferences. Ferrell et al. (2008) used crime in the home neighborhood as one of the factors for a binary logistic regression mode choice model based on data from the San Francisco area with mixed results for different communities. Crash data was not included in their model. Appleyard and Ferrell (2017) studied the impact of certain categories of crime on the mode choice for trips to and from Bay Area Rapid Transit (BART) stations in Oakland and Berkeley, finding that higher incidence of violent and property crimes is correlated with a lower percentage of pedestrians and cyclists. To the best of the authors’ knowledge, study has been conducted on the effect of crime and traffic collisions at the origin and destination of a trip on active mode choices using revealed preference data. This paper also features a new methodological contribution by conducting an analysis of the impact of crime on the consumer welfare of a set of transportation mode choice alternatives.

2013) collected detailed trip journals for households in New York City which are used for trip-related and demographic variables used in the model. Crime statistics were downloaded from the NYC OpenData web portal (NYC Open Data, 2017) and vehicle crash statistics were gathered from the NYPD website (Motor Vehicle Collisions, 2017). 4.1. Regional Household Survey The NYMTC Regional Household Survey is conducted approximately every ten years to collect socioeconomic data and travel patterns of residents in the New York City metropolitan area for travel demand forecasting. The most recent survey was conducted from September 2010 to November 2011 and contains 143,925 linked trips from 18,965 households in New York, New Jersey and Connecticut. The survey results are divided into several databases, including household information, individual person socioeconomic variables, and trip characteristics. This data does not contain exact travel dates or times for the trips to avoid possible identification of any survey participants (NYSDOT/ NYMTC, 2014). This is important to note because it prevented weather, which has proven significant in active mode choice, from being used as an explanatory variable in the model (Akar and Clifton, 2009). The data also does not contain route information for trips or specific origin and destination locations, but it does indicate the origin and destination census tracts. This project focuses only on commuter travel in the five boroughs of New York City, a densely populated area that features a wide range of ride-hailing services as well as a comprehensive public transit network that includes a bikesharing system. It is assumed that travellers within New York City have private vehicle (taxi or personal vehicle), transit, walking and cycling transportation options available to them, although in reality the bikesharing system is geographically concentrated in the most densely populated parts of Manhattan and Brooklyn. The NYMTC dataset included the purpose of each trip, allowing the data to be classified into commuter trips and non-commuter trips. This study considered only home-to-work and home-to-university trips beginning and ending within New York City limits. Any trip entries missing key information or trips by individuals who did not disclose certain demographic data were removed from the study dataset. There was a total of 3373 trips that met the criteria for this study. Trip adjustment factors provided by NYMTC were used to account for inconsistencies and under-reporting in the dataset. Any trips made in a private vehicle, such as drive alone, drive with passenger, taxi, etc. were considered to be the “Auto” mode. Trips made on any form of public transit were classified as “Transit” mode. Cycling and walking trips were classified as “Bike” and “Walk” modes respectively. The majority of trips (60.4%) were on transit, while 25.9% drove or were driven, 11.4% were walking trips and the remaining 1.7% of trips were made on a bicycle.

3. Problem description This project aims to quantify the effect that fear of crime has on people choosing active modes of transportation (walking and cycling) during their commute. The hypothesis being tested is that fear of crime does in fact influence travellers to choose auto or transit modes where they feel safer as they are less likely to be a victim of street crime. The secondary hypothesis is that the traveller utility function for mode choices includes variable parameters for the effects of different explanatory variables which is best represented using a mixed logit model. This is considered because of the heterogeneity of fear from crime across individuals, as well as different characteristics such as physical fitness which may impact whether a traveller chooses an active mode of transportation. Answering this question provides further evidence to be used in travel behavior modelling, traveller informatics design and municipal resource allocation.

4.2. New York City census tracts Census tracts are small areas roughly equivalent to a neighborhood established by the Federal Bureau of Census for analysing populations. There are 2167 census tracts within New York City. In this project they are used to link the latitude and longitude of crime and vehicle collision data with the trip origins and destinations in the Regional Household Survey. The census tract boundary information for New York City was obtained from the NYC Department of Planning (NYC Planning, 2017). 4.3. Crime data

4. Data collection and analysis

New York City crime data was downloaded for the 2009–2011 period. This is the duration of the Regional Household Survey used for trip information as well as the 20 months prior to the survey. The earlier data was included because perception of crime levels in an area are formed over time, so areas with high crime in 2009 would still be

The data used to estimate the random utility model came from three sources. The 2010–2011 New York Metropolitan Transportation Council (NYMTC) Regional Household Survey (NYSDOT/NYMTC, 38

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N.S. Caros and J.Y.J. Chow

prostitution, for which there were 448,551 total entries. The vast majority of the crimes were in either the assault (45.38%) or robbery (44.22%) categories. Geographic coordinates are given for the location of each incident, which was used to calculate a crime score for each census tract using the number of crimes weighted by severity. Because the National Institute of Justice contains scores for many variations on a single type of crime, an average severity score was taken for each crime category. The frequency and severity of each crime category are presented in Table 1. The spatial distribution of the crime score across census tracts is shown in Fig. 1. Densely populated areas such as lower Manhattan have high scores, as well as lower income neighborhoods such as the southern portion of the Bronx and eastern Brooklyn. The mean crime score for all census tracts is 2939 with a large standard deviation of 2511 and a minimum and maximum of 0 and 24268, respectively.

Table 1 Frequency and severity of crimes by category. Crime Category

Occurrences

% of Total

Severity Score

Arson Felony Assault Dangerous Weapons Robbery Murder Prostitution Total

4078 203,560 40,811 198,368 1525 209 448,551

0.91% 45.38% 9.10% 44.22% 0.34% 0.05% 100.00%

20.0 16.4 3.5 13.9 40.3 4.1

considered dangerous for a traveller in 2011 regardless of the actual crime level at that time (Skogan, 1986). This represents 1.4 million entries over the 3-year span. The statistical presence of crime in an area does not necessarily correlate with public perception of crime or fear of victimization in that area. Perception of the amount of crime and fear or crime involve distinct components of personal reaction to a crime problem (Erskine, 1974). For that reason, the total number of crimes cannot be used to approximate public perception and fear of crime. Crimes are weighted based on the National Institute of Justice study (Wolfgang et al., 1985) which surveyed Americans’ perceptions of crime severity, asking them to assign a severity score to different crimes and developed a widelyused crime severity index. For the purpose of this study, all administrative crimes (tax evasion, money laundering, etc.) and any low-level misdemeanours that were given a severity score less than 3.0 were assumed to have no effect on travel behaviour as they do not contribute to perceptions of threat to personal safety. The high severity crime categories considered by this study are: arson, felony assault, dangerous weapons charges, robbery (including grand larceny), murder, and

4.4. Vehicular crash data The New York Police Department (NYPD) database of all motor vehicle collisions (Motor Vehicle Collisions, 2017) was filtered to include only the 2009–2011 timeline and exclusively incidents in which either a pedestrian or cyclist was injured. A total of 27,670 records met the criteria. The total number of crashes that involved a pedestrian injury and the total number of crashes that involved a cyclist injury was calculated for each census tract. This data is presented spatially in Fig. 2. The results are expected; a higher incidence of collisions occurs in densely populated parts of Manhattan and Brooklyn. The effect of the total number of collisions on the choice of an active mode may be distorted by the fact that more collisions occur in hightraffic areas, even if the percentage of cyclists or pedestrians who are

Fig. 1. Crime score by census tract. 39

Travel Behaviour and Society 18 (2020) 37–45

N.S. Caros and J.Y.J. Chow

Fig. 2. Vehicle collisions involving pedestrian or cyclist injuries by census tract.

involved in a collision is lower. For example, the total number of collisions involving cyclists may be higher near high-traffic bike lanes simply because there are more cyclists in the vicinity, but the percentage of all bikers who are involved in collisions in this area is lower than in areas without bike lanes. To address this issue, the collision rate is also included as an explanatory variable in the model. This rate is determined by taking the total collisions in a census tract and dividing that by the total number of trips recorded in the Regional Household Travel Survey that start or end in that census tract for each mode. The mean rate of collisions with pedestrian injuries from 2009 to 2011 per trip recorded in the Regional Household Survey across all census tracts

is 1.2, and the mean rate of collisions with cyclist injuries per census tract is 1.4. 4.5. Trip cost calculation Perhaps the most important factor in mode choice decisions is the cost of each trip. The cost of each trip includes the user cost of time as well as any direct monetary cost for certain modes, such as transit fare or parking. To evaluate the cost of alternatives, cost was calculated based on trip distance, provided in the Regional Household Travel Survey, and an average urban peak hour cost per mile (Litman, 2009) shown in Table 2(a) and converted to 2010 dollars. These values were calculated for general urban areas and are a good approximation, but New York City has unique characteristics such as extreme peak hour traffic congestion, high parking costs and an extensive public transit network. The speed of transit and cycling often exceeds that of vehicle travel, especially during peak hour, so using these values may be somewhat different than the actual perceived cost of travelling in New York City. Specific values for New York City are not available and therefore the general values are used as estimates. The travel cost by trip is summarized in Table 2(b).

Table 2(a) Travel cost per mile. Travel Mode

Cost per Mile (2010 USD)

Cost Components

Avg. Speed (mph)

Auto

0.694

parking,

32

Transit Walk Bike

0.866 1.469 0.514

Travel time, operating Travel time, Travel time, Travel time,

fare operating operating

20 3 10

5. Methodology

Table 2(b) Travel cost by trip summary. Measure

Auto

Transit

Walk

Cycling

Mean Standard Deviation Maximum Minimum

4.035 3.254 27.050 0.069

5.038 4.062 33.770 0.087

8.544 6.888 57.269 0.147

2.989 2.410 20.034 0.051

To test the effect of the explanatory variables on mode choice decisions, a mixed logit random utility model is used. A popular statistical modeling software package called mlogit developed by (Croissant, 2012) for R is used to estimate the parameters and statistical significance for each explanatory variable as well as the overall fit of the model. The set of variables and each model is described in detail in the following sections.

Table 3 Traveller demographic distribution. Gender Male 1610

Age Female 1763

65+ 130

Income < 25 432

Car Owner

> $100 k 1096

< $30 k 693

40

Yes 2039

Driver's License No 1334

Yes 2479

No 894

Travel Behaviour and Society 18 (2020) 37–45

N.S. Caros and J.Y.J. Chow

5.1. Explanatory variables

logit model is calculated in Eq. (2).

Both alternative specific and individual specific variables are used in the mode choice model. The alternative specific variables are travel cost, sum of trip origin and destination census tract crime scores, sum of trip origin and destination census tract vehicle collisions, and the collision rate. The individual specific variables are dummy variables for age >65 , age <25, gender, annual income >$100, 000 , annual income >$30, 000 , car ownership and driver’s license holder. Some of these variables proved to be statistically insignificant and were left out of the final model. Table 3 presents the breakdown of the individual specific variables for the study trips. The sum of the trip origin and destination census tract crime scores, vehicle collisions and collision rates were used to estimate the magnitude of crime and collisions as detailed route information is not available in the Regional Household Survey. It is not a perfect substitute given that a long trip may start and end in areas with high collisions but travel primarily within low collision zones. Furthermore, studies that investigate area-based attributes are susceptible to the effects of the zoning scheme and how the zone delineation might differ from the “true casually relevant” geographic context (Kwan, 2012). This is known as the Uncertain Geographic Context Problem. These two factors may affect the link between perceived danger from crime and vehicular collisions, which we seek to measure, and the actual crime and collision data used as a substitute in the model. Quantifying these effects and mitigating them in future household travel surveys is beyond the scope of this paper and is a topic for further research. The impact of the Uncertain Geographic Context Problem may be limited due to the high resolution of the census tract zoning system. The average tract size is just 90 acres, approximately 16 square blocks (two avenues by eight streets) in the Manhattan grid system, therefore attribute variation within a tract is less likely than it would be with larger zones.

j

e

xjn

xjn

(2)

f (x i | )

(3)

In maximization the logarithmic version of this function is often used as it is an increasing function as shown in Eq. (4). n

max [LL ( ) =

ln(f (xi | ))] i=1

(4)

Estimation of coefficients in a mixed logit model uses maximum likelihood estimation (MLE), but mixed logit probabilities cannot be calculated as a closed-form expression and are simulated instead. Probabilities are approximated by drawing values from the probability density function many times and averaging the results. Halton draws are used rather than simple random draws as they have proven to be the most efficient (Bhat, 2003). Taking 100 draws for each estimation provided consistent results. 5.3. Model evaluation The mixed logit model is evaluated for goodness of fit using the loglikelihood values described in previous sections as well as the McFadden R2 ( 2 ), which is calculated using Eq. (5). 2

=1

lnLc lnLnull

(5)

Lc denotes the maximized likelihood value from the tested model and Lnull denotes the maximized likelihood of the null model, a model with only an intercept and no other alternative- or user-specific characteristics or covariates. A higher McFadden R2 indicates that the model is a closer fit to the data (Train, 2009). Another measure of goodness of fit is the likelihood ratio test (Neyman and Pearson, 1928). This test compares the goodness of fit of a specified model to that of a null model. The ratio of the tested likelihood to the null likelihood, as shown in Eq. (6), is compared to the Chi-Square distribution using Eq. (7) to determine a confidence interval. Comparison to the Chi-Square distribution establishes whether there is a significant difference between expected values and observed values. The likelihood ratio test is used in Section 6 to evaluate whether coefficients in the mixed logit model are

xin '

f ( | )d

'

i=1

'

e

e

n

L( ) =

The multinomial logit (MNL) model is a regression model that uses a utility function with a Gumbel distributed random disturbance to calculate the probabilities of a user choosing a particular option among of a set of alternatives. The representative utility of each alternative for each user is calculated based on alternative-specific (such as travel time) and user-specific (such as personal income) characteristics and estimated coefficients defining the contribution of each characteristic to utility. The random disturbance, used to explain variation in tastes across the population, is added to the representative utility to calculate the random utility. The probability of choosing option i among J alternatives is then calculated using Eq. (1). J j =1

xin

In the above equation, f ( | ) d is the probability density function for the random coefficients given a set of distribution parameters . Normal and log-normal are common distributions assumed, although uniform, triangular and others can also be used. For this study, a normal distribution was found to fit the data best according to the evaluation techniques discussed further in Section 5.3. The model allows for correlated or uncorrelated coefficients; both were tested for this study but since the correlated coefficients were a better fit only that is reported. Given a set of observations of the attributes for each alternative and the chosen mode, Maximum Likelihood Estimation (MLE) can be used to estimate the model coefficients (Scholz, 2006). It estimates the coefficients to maximize the likelihood of making the observations given the parameters. Given identically and independently distributed observed values of x1, x2 , , xn and some coefficient vector , the likelihood of observing x1, x2 , , xn given is shown in Eq. (3).

5.2. Logit models

Pin =

'

e

Pin =

(1)

Mixed logit models are a more recently developed type of logit model that, while more computationally complex than MNL, can overcome some of the limitations that MNL exhibits. A mixed logit model uses random coefficients to represents taste variation among travellers and does not assume independence of random disturbances among alternatives. Train (2009) provides an overview of the mixed logit model and estimation techniques. The probability of an individual n selecting a certain mode i among a set of j alternatives in a mixed

41

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N.S. Caros and J.Y.J. Chow

correlated or uncorrelated.

L = c Lnull 2

=

2ln

Table 4(a) Mixed logit model estimation summary.

(6)

Variable

Estimate

t-statistic

p-value

(7)

Intercept (Bike) Intercept (Transit) Intercept (Walk) Mean of Travel Cost Mean of Total Collisions Mean of Collision Rate Mean of Crime Score × 10−3 High Income (Bike) High Income (Transit) Male (Bike) High Age (Walk) Low Age (Walk) Car Owner (Bike) Car Owner (Transit) Car Owner (Walk) Driver’s License (Bike) Driver's License (Transit) Driver's License (Walk)

2.3899 5.0096 4.7614 −0.4372 0.2366 −0.2185 −0.2909 0.9216 0.2978 1.5872 −0.9966 0.5230 −4.2179 −2.7163 −2.7414 −2.2881 −1.9684 −2.1713

3.4080 32.1994 17.8346 −11.3117 4.6152 −3.0499 −5.1876 2.1963 4.8586 3.3943 −2.3263 2.8999 −9.9954 −21.0034 −14.4937 −4.9118 −15.3269 −11.6803

*** *** *** *** *** ** *** * *** *** * ** *** *** *** *** *** ***

The two-tailed t-statistic is used to test the hypothesis that a particular variable is statistically significant within a 95% confidence level. Variables that are not significant at that level are removed from the model estimation and another iteration is estimated. This ensures that the final presented model contains only statistically significant variables. For the normally distributed random coefficients in a mixed logit model, the t-statistic for the mean and correlation of the coefficient are used rather than the t-statistic for the coefficient itself (Washington et al., 2011). A method of analysing model results is to evaluate the social welfare of a set of choices or observed variables. Social welfare (alternatively referred to as consumer surplus, trip satisfaction or measure of accessibility) is the difference between what a traveler is willing to pay in terms of overall travel cost and the travel costs that they incur, and is used to measure the benefits of a new or improved transportation network. There is significant literature where social welfare is used for transportation mode choice. The concept of applying social welfare to transportation project cost-benefit analysis was first proposed by McFadden (1973). Small and Rosen (1981) gives an in-depth overview of the method. Niemeier (1997) provides an example of a logit model estimated using actual data for transportation systems in Puget Sound with evaluation of social welfare. Given a model estimation, the change in social welfare due to changes in attributes can be quantified. The social welfare of a model with choice set Cn for an individual n in units of trip cost is calculated

Significance Codes: *** = < 0.001 ** = < 0.01 * = < 0.05 Number of Observations 3373 Model Log-Likelihood −2554.9 Null Log-Likelihood −3293.3 2 Likelihood Ratio χ 1476.7 2 McFadden R 0.2242 0.2203 Adjusted R2

cars are less likely to choose non-auto alternatives, as are those travelers with a drivers’ license. Similarly, young travelers are more inclined to choose walk while older travelers are not. The correlation between alternative variables is mixed and only one is statistically significant. Negative correlation between crime and travel cost might be explained by the travel patterns in high crime neighborhoods. In Manhattan, the low travel cost auto mode is not very practical due to congestion and high parking costs. Residents of the poorer neighborhoods of the Bronx and Brooklyn lack the resources to purchase and operate a personal vehicle or travel by taxi. In both cases, it stands to reason that travelers would favor transit and walking, which have higher travel costs per Table 2(a), causing a strong correlation between crime and travel cost. The overall elasticities of choosing active modes to different model parameters are shown in Table 5. The distribution of elasticities across different demographic groups is presented in Fig. 3, with the walk mode elasticities plotted on the secondary axis for ease of comparison. Travellers’ choice to walk generally becomes more elastic to crime with increasing age and wealth, but the opposite is true of cycling. Manhattanites’ decision to walk is less elastic towards crime than residents of the other boroughs, but more elastic when choosing cycling. Travellers of American Indian descent have the least elastic decisionmaking for walking, whereas Hispanic and African-Americans are more elastic in their cycling choices compared to other races. The change in social welfare due to change in crime rates can be determined using the estimated mixed logit model. As with elasticity, the social welfare for the model was approximated by simulation of the integral over the distribution of the random coefficients. Fig. 4 shows the mean social welfare increase per person per trip if the crime score in a New York City census tract was reduced to zero. In other words, eliminating all crime within a census tract could potentially increase social welfare by a range of $0.00 to $0.26 per trip per person. A tract in the Co-op City neighborhood in the Bronx is shown to have the greatest impact on social welfare with the remaining top 10 tracts in Manhattan.

using Eq. (8), where cost is the mean of the random coefficient pertaining to cost and Vin is the representative utility of choice i for user n . Taking the sum of welfare En across all individuals gives the total social welfare of a set of choices for each trip. Comparing social welfare for different models can be used to determine where a limited set of resources can be applied to achieve the greatest effect.

En =

1 cost

eVin

ln i Cn

***

(8)

6. Results The mixed logit model estimated the parameters of the coefficient distributions using simulation with 100 Halton draws. The mlogit package allows mixed logit estimation with or without assuming correlation between the alternative specific variables in the model. When the variables are assumed to be correlated, the model outperforms both the standard multinomial logit model and non-correlated mixed logit model in goodness of fit. A likelihood ratio test between the models found that the mixed logit correlated model is a better fit with 99.9% confidence and therefore we reject the hypothesis that the variables are not random and uncorrelated. A summary of the random coefficient means, significance and the model evaluation results are shown in Table 4(a), and the correlation matrix is shown in Table 4(b). The covariance matrix is shown in Table 4(c). The mean coefficient for travel cost, crime rate and collision rate were all found to be negative and highly significant, indicating that travelers are less likely to choose non-auto modes over the auto mode for trips where these variables are perceived to be higher. This confirms the hypothesis that the presence of high crime and high collision areas deter active mode choice decisions. Unsurprisingly, travelers who own

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Table 4(b) Alternative specific variable correlation matrix. Parameter

Travel Cost

t-stat

Crime

t-stat

Collisions

t-stat

Coll. Rate

t-stat

Travel Cost Crime Collisions Collision Rate

1.0000 −0.8275 0.0807 0.6261

– 2.6511 −0.1377 −1.1508

−0.8275 1.0000 −0.6260 −0.2913

2.6511 – −1.5536 0.3650

0.0807 −0.6260 1.0000 −0.3771

−0.1377 −1.5536 – 0.5872

0.6261 −0.2913 −0.3771 1.0000

−1.1508 0.3650 0.5872 –

7. Conclusions and future work

Table 4(c) Alternative specific variable covariance matrix. Parameter

Travel Cost

Crime

Collisions

Coll. Rate

Travel Cost Crime Collisions Collision Rate

0.0922 −0.0674 0.0002 0.0243

−0.0674 0.0720 −0.0017 −0.0100

0.0002 −0.0017 0.0001 −0.0005

0.0243 −0.0100 −0.0005 0.0164

In an effort to determine whether fear of crime or being struck by a vehicle affects the decision to use an active mode of transportation, a mixed logit model was estimated for travellers using data from New York City between 2010 and 2011. The flexibility of the mixed logit model to include correlation between the variables allowed it to model the mode choice decisions of this set of travellers better than multinomial logit. Several explanatory variables were included in the model: traveller demographic information, travel cost and the incidence of crime and vehicle collisions involving pedestrians and cyclists. Incidence of severe crimes at the origin and destination of a trip was found to discourage travellers from choosing an active mode of transportation or transit at a statistically significant level. The finding implies that travellers could be encouraged to cycle by reducing crime levels, or by being provided an alternative route with less crime. By dividing the crime rate coefficient by the cost coefficient, it can be determined that travellers are willing to pay $0.66 for a 1000-point reduction in crime severity. An increase in crime of 1% has a much greater impact on bike ridership (2.11% reduction) than on walking

Table 5 Elasticity of choosing active mode to different variables. Walk Mode Elasticity

Mean

SD

Mean

SD

−1.7168 −2.1064 −3.6036 −7.5547

1.3777 1.2842 2.9910 5.3507

−0.5427 −0.0577 −0.0183 −0.4621

2.3159 0.2211 0.1998 1.6547

Elasticity to Crime Score (x1000)

Travel Cost Crime Total Collisions Collision Rate

Bike Mode Elasticity

-1.95

-0.01

-2

-0.02

-2.05

-0.03

-2.1

-0.04

-2.15

-0.05

-2.2

-0.06

-2.25

Bike

-0.07

-2.3

Walk

-0.08 -0.09

-2.35

-1.8

-0.01

-1.85

-0.02

-1.9

-0.03

-1.95

-0.04

-2

-0.05

-2.05

-0.06

-2.1

-2.15

-0.04 -0.06 -0.08

-1.5

-0.1 -0.12

-2

-0.14

-2.5

-3

BK

BX

MH

Bike

-0.16

Walk

-0.18

QU

SI

Other

Elasticity to Crime Score (x1000)

Elasticity to Crime Score (x1000)

-0.02

-1

-0.08

Walk

-0.09

Traveler's Age Range (Years) 0

-0.5

-0.07

Bike

-2.2

Traveler's Household Income ($1000's/year) 0

0

-1.75

0

-1.9

Elasticity to Crime Score (x1000)

Parameter

0 -0.5 -1

0 Bike

-0.01

Walk

-0.02 -0.03

-1.5

-0.04 -0.05

-2

-0.06

-2.5

-0.07

-3

-0.08

-0.2

Traveler's Home Borough

Traveler's Race

Fig. 3. Mean elasticity variation across demographic groups (BK: Brooklyn, BX: Bronx, MH: Manhattan, QU: Queens, SI: Staten Island). 43

Travel Behaviour and Society 18 (2020) 37–45

N.S. Caros and J.Y.J. Chow

Fig. 4. Change in social welfare due to crime elimination by census tract.

Appendix A. Supplementary data

(0.06% reduction). Removing crime completely would improve a traveler’s trip satisfaction by as much as $0.26 per trip. By displaying crime informatics to travellers, public agencies could encourage the use of active transportation modes and reduce the negative societal impacts of automotive travel. Compared to crime, collision rate has a much stronger impact on bike ridership, with an elasticity of −7.56 (3.6 times higher elasticity than crime). Future research into the implications of this study are warranted. Development of an accurate travel cost function for different modes in New York City would be beneficial for future research. As with any choice model, additional explanatory variables could be tested to improve the fit; the Regional Household Survey contains a plethora of demographic and trip-specific information which could be included. Other sources of trip data with date and time information would allow the effect of weather to be included. Finally, a study of actual traveller behavior when provided the crime and collision information for their route (through Google Maps or some other API) would provide strong evidence whether this information is important in mode choice decision making.

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