Chapter 29: 

Crime Mode Split 

Ned Levine 

Ned Levine & Associates 

Houston, TX 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table of Contents 

Theoretical Background 29.1 

Utility of Travel and Mode Choice 29.1

 Discrete Choice Analysis 29.4 

 Multinomial Logit Function 29.5 

 Generalized Relative Utility Function 29.7 

 Measuring Travel Costs 29.8 

Aggregate and Individual Utility Functions 29.9 

Tools for Estimating Mode Split in CrimeStat 29.11 

Relative Accessibility 29.11 

Hierarchical Approaches to Estimating Mode Accessibility 29.12 

Spreadsheet for Estimating Mode Split Impedance Values 29.15 

 Define travel modes 29.15 

 Define target proportions 29.17 

 Other studies 29.17 

 Journey to work census 29.18 

 Select mode functions 29.20 

 Select model priorities 29.21 

 Iteratively estimate parameters 29.22 

 Examine the graphs in the spreadsheet 29.22 

Adapting spreadsheet for travel time or travel cost 29.28 

 Empirically Estimating the Mode-specific Impedance 29.28 

CrimeStat IV Mode Split Tools 29.28

 Mode Split Setup 29.30 

 Constrain Transit Trips to Network 29.30 

Default 29.30 

 Constrain to network 29.30 

 Accurately defined transit networks 29.31 

 Utility for creating transit network 29.31 

 Entering the network parameters 29.32 

 Minimum absolute impedance 29.32 

 Applying the Relative Accessibility Function 29.33 

Usefulness of Mode Split Modeling of Crime Trips 29.37 

Limitations to the Mode Split Methodology for Crime Analysis 29.41 

Conclusions 29.43 

References 29.44 
 

 

Chapter 29: 

Crime Mode Split 

In this chapter, the third modeling step in the crime travel demand model is discussed, 

mode split. Mode split involves separating (splitting) the predicted trips from each origin zone to 

each destination zone into distinct travel modes (e.g., walking, bicycle, driving, train, bus). 

This model has both advantages and disadvantages for crime analysis. At a theoretical 

level, it is the most developed of the four stages since there has been extensive research on travel 

mode choice. For crime analysis, on the other hand, it represents the >weakest link= in the 

analysis since there is very little available information on travel mode by offenders. Since 

researchers cannot interview the general public in order to document crimes committed by 

respondents nor, in most cases, even interview offenders after they have been caught, there is 

very little information on travel mode by offenders that has been collected.1

 Consequently, we 

have to depend on the existing theory of travel mode choice and adapt it intuitively to crime data. 

The approach is solely theoretical and depends on the validity of the existing theory and on the 

intuitiveness of guesses. Hopefully, in the future, there will be more information collected that 

would allow the model to be calibrated against some real data. But, for the time being, we are 

limited in what can be done. 

Theoretical Background 

The theoretical background behind the mode split module is presented first. Next, the 

specific procedures are discussed with the model being illustrated with data from Baltimore 

County. 

Utility of Travel and Mode Choice 

The key aim of mode choice analysis is to distinguish the travel mode that travelers (or, 

in the case of crime, offenders) use in traveling between an origin location and a destination 

location. In the travel demand model, the choice is for travel between a particular origin zone and 

There is no reason this data could not be collected. Typically, many police departments collect information 

on >Method of departure= from a crime scene. When a police report is taken, the victim is sometimes asked 

how the offender left the scene. In most cases, the information is not recorded on the police forms, or at 

least those that have been examined. This information is probably unreliable in any case since many 

offenders will take the bus or leave their car nearby while they walk/run to the crime scene. Still, if police 

departments were to put more effort into collecting this information and, perhaps, to validating it with 

arrested offenders, then it is possible to build up reliable data sets that can be used to model mode split. 

Until then, unfortunately, we have to rely on theory rather than evidence. 

29.1 

1 
 

a particular destination zone. Thus, the trips that are distributed from each origin zone to each 

destination zone in the trip distribution module are further split into distinct travel modes. 

With few exceptions, the assumption behind the mode split decision is for a two-way trip. 

That is, if an offender decides on driving to a particular crime location, we normally assume that 

this person will also drive back to the origin location. Similarly, if the offender takes a bus to a 

crime location, then that person will also take the bus back to the origin location. There are, of 

course, exceptions. A car thief may take a bus to a crime location, then steal a car and drive 

back. But, in general, without information to the contrary, it is assumed that the travel mode is 

for a round trip journey. 

Underlying the choice of a travel mode is assumed to be a utility function. Chapter 21 

discusses the economic theory of utility choice, so the discussion here will be brief. Essentially, 

mode split utility is a function that describes the benefits and costs of travel by that mode 

(Ortuzar & Willumsen, 2001). This can be written with a conceptual equation: 

 (29.1) 

where >f= is some function of the benefits and the costs. The benefits have to do with the 

advantages in traveling to a particular destination from a particular origin while the costs have to 

do with the real and perceived costs of using a particular mode. Since the benefits of traveling 

to a particular destination from a particular origin are probably equal, the differences in utility 

between travel modes essentially represent differences in costs (Train, 2009). Thus, Equation 

29.1 breaks down to: 

 (29.2) 

where ‘F’ is another function but this time of only the costs. If different travel modes (e.g., 

driving, biking, walking) are each represented by a separate utility cost function, then they can be 

compared: 

 (29.3a)

 (29.3b)

 (29.3c) 

 (29.3l) 

29.2 
 

 

 

 

where Utility1 through UtilityL represents l distinct travel modes, cost1through costk represent k 

cost components and are variables, and F1 through Fl represent l different utility functions (one 

for each mode). 

There are several observations that can be made about this representation. First, each of 

the cost components can be applied to all modes. However, the cost components are variables 

in that the values may or may not be the same. For example, if cost1 is the operating cost of 

traveling from an origin to a destination, the cost for a driver is, of course, a lot higher than for a 

bus passenger since the latter person shares that cost with other passengers. Similarly, if cost2 is 

the travel time from a particular origin zone to a particular destination zone, then travel by 

private automobile may be a lot quicker than by public bus. Time differences can be converted 

into costs by applying some type of hourly wage/price to the time. To take one more example, 

for driving mode, there could be a cost in parking (e.g., in a central business district); for transit 

use, on the other hand, this cost component is zero. In other words, each of the travel modes 

has a different cost structure. The same costs can be enumerated, but some of them will not 

apply (i.e., they have a value of 0). 

Second, the costs can be perceived costs as well as real costs. For example, a number of 

studies have demonstrated that private automobile use is seen by most people as far more 

convenient to than bus or train (e.g., see Schnell, Smith, Dimsdale, & Thrasher, 1973; Roemer & 

Sinha, 1974; WASHCOG, 1974; Carnegie-Mellon University, 1975; Johnson, 1978; Levine & 

Wachs, 1986). >Convenience= is defined in terms of ease of access and effort involved in travel 

(e.g., how long it takes to walk to a bus stop from an origin location, the number of transfers that 

have to made to reach a final destination, and the time it takes to walk from the last bus stop to 

the final destination). While it is sometimes difficult to separate the effects of convenience 

from travel itself, it is clear that most people perceive this as a dimension in travel choice. In 

turn, convenience can be converted into a monetary value in order to allow it to be calculated in 

a cost equation, for example how much people are willing to pay in time savings to yield an 

equivalent amount of convenience (e.g., asking how many more minutes in travel time by bus an 

individual would be willing to absorb in order to give up having to drive). 

Third, these costs can be considered at an aggregate as well as individual level. At an 

aggregate level, they represent average or median costs (e.g., the average time it takes to travel 

between zone A and zone B by private automobile, bus, train, walking, or biking; the average 

dollar value assigned by a sample of survey respondents to the convenience they associate in 

traveling by car as opposed to bus). 

On the other hand, at an individual level, the costs are specific to the individual. For 

example, travel time differences between car and bus can be converted into an hourly wage using 

29.3 
 

the individual=s income (i.e., someone making $100,000 a year is going to price that time savings 

differently than someone making only $25,000 a year). 

Fourth, a more controversial point, the specific mathematical function that ties the costs 

together into a particular utility function may also differ. Typically, most travel demand models 

have assumed that a similar mathematical function is used for all travel modes; this is the 

negative exponential function described below (Ortuzar and Willumsen, 2001; Domencich & 

McFadden, 1975). However, there is no reason why different functions cannot be used. Thus, 

the equations above identify different functions for the modes, F1 through FL. One can think of 

this in terms of weights. Each of the different mathematical function weigh the cost 

components differently. 

It is an empirical question whether individuals apply different functions to evaluating the 

different modes. For example, most people would not drive just to travel one block (unless it 

was pouring rain or unless a heavy object had to be delivered or picked up). Even though it is 

convenient to get into a vehicle and drive the one block, most people see the effort involved 

(and, most likely, the fuel and oil costs) as not being worth it. 

In other words, it appears that a different utility function is being applied to walking as 

opposed to driving (i.e., walk up to a certain distance; drive thereafter). A strict utility theorist 

might disagree with this interpretation saying that the per minute cost of walking the one block 

and back was less than the monetized per minute cost of operating the vehicle (which may 

include opening a garage door, getting into the vehicle, starting the vehicle, driving out of the 

parking spot, closing the garage door, and then driving the one block). In other words, it could 

be argued that the difference in behavior has to do with the values of the different cost 

components, rather than the way they are weighed together (the mathematical function). In 

retrospect, one can explain any difference. It is argued in this chapter, however, that crime trips 

appear to show different likelihoods by travel mode and that treating each of these functions as 

distinct allows more flexibility in the framework. 

Discrete Choice Analysis 

No matter how the utility functions are defined, they have to be combined in such a way 

as to allow a discrete choice. That is, an offender in traveling from zone A to zone B makes a 

discrete choice on travel mode. There may be a probability for travel by each mode, for 

example 60% by car and 40% by bus. But, for an individual, the choice is car or bus, not a 

probability. The probabilities are obtained by a sample of individuals, for example of 10 

individuals 6 went by car and 4 went by bus. But, still, at the individual level, there is a distinct 

choice that was made. 

29.4 
 

Multinomial Logit Function 

A common mathematical framework that used is for mode choice modeling at an 

aggregate level is the multinomial logit function (Ortuzar & Willumsen, 2001; Oppenheim, 1980; 

Domincich & McFadden, 1975; Stopher & Meyburg, 1975). Chapter 21 on discrete choice 

modeling discussed this model extensively but for individual decision makers. If there was data 

available on the individual travel mode choices made by offenders, then an individual level 

discrete choice model could be constructed. However, till now, such data has not been 

available. Consequently, the modeling has to occur for zone-to-zone interactions rather than for 

individuals choosing among zones. 

The multinomial logit model used is for aggregate zone-to-zone flows. That is, for all 

trips from zone ݅ to zone ݆, a multinomial model can be defined as: 

(29.4)

where PijL is the probability of using a mode for any particular trip link (particular origin zone 

݅	to particular destination ݆), L is the travel mode, Vni is the representative utility (that observed 

by the researcher/analyst as opposed to total utility which includes unobserved factors) for zone 

݅, among ݆	alternative destinations. The representative utility, in turn, is seen as a linear 

combination of independent variables (predictors) for travel from origin zone ݅, to destination 

zone ݆:

(29.5) 

where VijL is the utility of traveling from origin zone ݅, to destination zone ݆, L is the travel 

mode, and βjL are coefficients. 

Substituting equation 29.5 into equation 29.4, we have: 

 (29.6)

which relates the linear combination for any one mode to the sum of the linear combinations of 

all modes. 

Several observations can be made about this function. First, the multinomial logit 

model relates the choice of alternatives to differences in the characteristics of the zones, both 

29.5 
ఉ

origin and destination (the Xijl), rather than to the differences in the destination zones themselves. 

This is different than the more general conditional logit model which relates the choices to the 

characteristics of the alternatives (destinations) and to interactions between the origin zones and 

the destination zones (and is also discussed in Chapter 21). 

Second, each travel mode, L, has its own costs and benefits and can be evaluated by 

itself. That is, each origin zone have a different set of destination alternatives according to the 

characteristics of both the origin and destination zones. That is, there is a distinct utility 

function for each mode. This is the numerator of Equation 29.6. However, the choice of any 

one mode is dependent on its utility value relative to other modes (the denominator of the 

equation). The more choices that are available, obviously, the lower the probability that a 

particular origin-destination zone combination will have for that mode. But the value 

associated with the mode (the utility) does not change. As mentioned above, we generally 

assume that the benefit of traveling between any two zones is identical for all modes and, hence, 

any differences are due to costs. 

Third, the mathematical form is the negative exponential. The exponential function is a 

growth function in which growth occurs at a constant rate (either positive - growth, or negative - 

decline). The use of the negative exponential assumes that the costs are related to the 

likelihood as a function that declines at a constant rate. It is actually a >disincentive= or 

>discount= function rather than a utility function, per se. That is, as the costs increase, the

probability of using that mode decreases, all other things being equal. Still, for historical

reasons, it is still called a utility function.

Fourth, for any one mode, the total cost is a logarithmic function of individual cost 

components: 

(29.7) 

(29.8) 

where the cumulative cost is made up of independent predictors X1, X2 through XJ, and β1 

through βk are coefficients for the individual cost components. Thus, we see that the utility 

function is a loglinear model, as was seen in Chapter 13. Thus, the utility function is Poisson 

distributed, declining at a constant rate with increasing cumulative costs. Domincich and 

McFadden (1975) point out that the error term is not Poisson distributed, but skewed as a Type I 

extreme value distribution (sometimes called a Gumbel distribution; Train, 2009). As discussed 

in Chapters 16-17, there are a variety of different Poisson models that incorporate skewed error 

29.6 
 

 

 

terms (Poisson-Gamma, Poisson-lognormal, COM-Poisson) and which could also be used to 

fine-tune the fit. Nevertheless, the mean utility is a Poisson-type function. 

Generalized Relative Utility Function 

One can generalize this further to allow any type of mathematical function. While the 

Poisson has a long history and is widely used, allowing other non-linear functions allows greater 

flexibility. It is possible that individuals apply different weighting systems in evaluating different 

modes (e.g., a negative exponential for walking, but a lognormal function for driving). We 

certainly see what appear to be different functions when the actual travel behavior of individuals 

are examined (e.g., homeless individuals don=t walk everywhere even though the cost of walking 

long distances is cheaper in travel time than taking a bus2

; people don=t drive or take a bus for 

very short distances, say a block or two). Therefore, if we allow that there are different travel 

functions for different modes, then more flexibility is possible than by assuming a single 

mathematical function. 

We can, therefore, write a generalized relative utility function as: 

(29.9)

where the terms are the same as in 29.4 except the function, FL, is some function that is specific 

to the travel mode, L. The numerator is defined as the impedance of mode L in traveling from 

origin zone ݅ to destination zone ݆ while the denominator is the sum of all impedances. 

Notice that the ratio of the cost function for one mode relative to the total costs is also the 

ratio of the impedance for mode L relative the total impedance. The total impedance was 

defined in Chapter 28 as the disincentive to travel as a function of separation (distance, travel 

time, cost). We see that the share of a particular mode, therefore, is the proportion of the total 

impedance of that mode. This share will vary, of course, with the degree of separation. For 

any given separation, there will usually be a different share for each mode. For example, at low 

separation between zones (e.g., zones that are next to each other), walking and biking are much 

more attractive than taking a bus or a train and, perhaps even driving. At greater separation 

(e.g., zones that are 5 miles apart), walking and biking are almost irrelevant choices and the 

likelihood of driving or using public transit is much greater. In other words, the share that any 

one mode occupies is not constant, but varies with the impedance function. 

In a survey of the travel behavior of homeless persons, it was noted that most homeless walked very short 

distances over the day even though the value of their time was very low. For longer trips, they still tended 

to take the bus rather than walk. Survey on the travel behavior of very low income individuals. Urban 

Planning Program, University of California at Los Angeles, 1987 (with Martin Wachs). 

29.7 

2 
Why then cannot the mode split be estimated directly at the trip distribution stage? If 

the trip distribution function is: 

(28.12 repeat) 

and if these trips, in turn, are split into distinct modes using equation 29.9, could not 28.12 be 

re-written as: 

(29.10)

where TijL is the number of trips traveling from origin zone ݅ to destination zone ݆ by mode L, 

Pi is the production capacity of zone ݅, Aj is the attraction of zone ݆, α and β are constants that are 

applied to the productions and attractions respectively, λ and τ are >fine tuning= exponents of the 

productions and attractions respectively, and IijL, is the impedance of using mode L to travel 

between the two zones? The answer is, yes, it could be calculated directly. If IijL was a 

perfectly defined impedance function (with no error), then the mode share could be calculated 

directly at the distribution stage instead of separating the calculations into two distinct stages. 

The problem, however, is that the impedance functions are never perfect (far from it, in fact) and 

that re-scaling is required both to get the origins and destinations balanced in the trip distribution 

stage and to ensure that the probabilities in equation 29.10 add to 1.0. The effect of these 

adjustments generally throws off a model such as equation 29.10. Consequently, the trip 

distribution and mode split stages are usually calculated as separate operations. 

Measuring Travel Costs 

The next question is what types of travel costs are there that define impedance? As 

mentioned above, there are real as well as perceived costs that affect a travel mode decision. 

Some of these can be measured easily, while others are very difficult requiring detailed surveys 

of individuals. Among these costs are: 

1. Distance or travel time. As mentioned throughout this discussion, distance is

only a rough indicator of cost since it is invariant with respect to time. Actual

travel time is a much better indicator because it varies throughout the day and can

be easily converted into a travel time value, for example by multiplying by an

average unit wage.

2. Other real costs, such as the operating costs of a private vehicle (fuel, oil,

maintenance), parking, and insurance. Some of these can be subsumed under

travel time value by working out an hourly price for travel.

29.8 
 

 

3. Perceived costs, such as convenience, fear of being caught by an offender, ease of

escape from a crime scene, difficulties in moving stolen goods, and fear of

retaliation by other offenders or gangs).

Some of these costs can be measured and some cannot. For example, the value of travel 

time can be inferred from the median household income of a zone for aggregate analysis or from 

the actual household income for individual-level analysis. Parking can be averaged by zone. 

Insurance costs can be estimated from zone averages if the data can be obtained. 

Many perceived costs also can be measured. Convenience, for example, could be 

measured from a general survey. The fear of being caught can be inferred from the amount of 

surveillance in a zone (e.g., the number of police personnel, security guards, security cameras). 

Even though it may be a difficult enumeration process, it is still possible to measure these costs 

and come up with some average estimate. 

Other perceived costs, on the other hand, may not be easily measured. For example, the 

fear an offender belonging to one gang has about retaliation from another gang is not easily 

measured. If one could map the ‘territory’ of a gang, then one could members of one territory 

would not commit a crime in another territory (Bernasco & Block, 2009). Similarly, the cost of 

moving stolen goods by a thief is not easily measured; one would need to know the location of 

the distributors of these goods. 

In practice, travel modelers make simple assumptions about costs because of the 

difficulty in measuring many of them. For example, travel time is taken as a proxy for all the 

operating costs. Parking costs can be incorporated through simple assumptions about the 

distribution across zones (e.g., zones within the central business district - CBD, are given an 

average high parking costs; zones that are central, but not in the CBD, are assigned moderate 

parking costs; zones that are suburban are assigned low parking costs). It would be just too 

time consuming to document each and every cost affecting travel behavior, particularly if we are 

developing a model of offender travel. 

Nevertheless, theoretically, these are all potentially measurable costs. They are real and 

probably have an impact in the travel decisions that offenders make. As researchers and analysts, 

we have to work towards articulating as many of these costs as possible in order to produce a 

realistic representation of offender travel. 

Aggregate and Individual Utility Functions 

One of the big debates in travel modeling is whether to use aggregate or individual utility 

functions to calculate mode share. The aggregate approach measures common costs for each 

29.9 
 

 

 

zone, assuming an average value. The disaggregate approach (sometimes called >second 

generation= models) measures unique costs for individuals, then sums upward to yield values for 

each zone pair. Even though the end result is an allocation of costs to each zone pair, the 

articulation of unique costs at the individual level can, in theory, allow a more realistic 

assessment of the utility function that is applied to a region. 

The aggregate approach will measure costs by averages. Thus, a typical equation for 

driving mode might be: 

 (29.11) 

where Tij is the average travel time between two zones, ݅ and ݆, and Pij is the average parking 

cost for parking in zone j. Notice that there are a limited number of variables in an aggregate 

model (in this case, only two) and that the assigned average is for an entire zone. Notice also that 

the parking cost is applied only to the destination zone. It is assumed that any traveler will pay 

that fee in that zone irrespective of which origin zone he/she came from. 

A disaggregate approach can allow more cost components, if they are measured. Thus, 

a typical equation for driving mode might be: 

 (29.12) 

where Tijk is the travel time for individual k between two zones, ݅ and ݆, Pij is the average parking 

cost for parking in zone ݆, Cijk is the convenience of traveling to zone ݆ from zone ݅ for 

individual ݇, Mijk is the comfort and privacy experienced by individual k in traveling from zone

݅ to zone ݆, and Sijk is the perceived safety experienced by individual k in traveling from zone ݅

to zone ݆. Notice that there are more cost variables in the equation and that the model is 

targeted specifically to the individual, k. Two individuals who live next door to each other and 

who travel to the same destination may evaluate these components differently. If these 

individuals have substantially different incomes, then the value of the travel time will differ. If 

one values privacy enormously while the other does not, then the cost of driving for the first is 

less than for the second. Similarly, convenience is affected by both travel time and the ease of 

getting in and out of vehicle. Finally, the perception of safety may differ for these two 

hypothetical individuals. There are many studies that have documented the significant role 

played by safety in affecting, particularly, transit trips (Levine & Wachs, 1986). 

In other words, the aggregate approach applies a very elementary type of utility function 

whereas the disaggregate approach allows much more complexity and individual variability. 

29.10 
 

Of course, one has to be able to measure the individual cost components, a difficult task under 

most circumstances. 

There is also a question about which approach is more accurate for correctly forecasting 

actual mode splits. Historically, most Metropolitan Planning Organizations have used the 

aggregate method because it=s easier. However, more recent research (McFadden, 2002; 

Ben-Akiva & Lerman, 1985; Domincich & McFadden, 1975) has suggested that disaggregate 

modeling may be more accurate. At the very minimum, the disaggregate model is more 

amenable to policy interpretations because it is more behavioral. If one could interview travelers 

with a survey, then it is possible to explore the variety of cost factors that affect a decision on 

both destination and mode split, and a more realistic (if not unique) utility function derived. 

But, as mentioned above, with crime trips, this is very difficult, if not impossible, to do. 

Consequently, for the time being, we are stuck with an aggregate approach towards modeling the 

utility of travel by offenders. 

Tools for Estimating Mode Split in CrimeStat 

CrimeStat has two sets of tools for estimating the mode split model. First, if individual 

level data on travel modes can be obtained, then the multinomial logit model in the Discrete 

Choice module can be used (see Chapters 21 and 22). That is, if data on actual mode choices 

taken by offenders could be obtained with characteristics of both the offenders and the zones in 

which they lived or committed crimes in being associated with those choices, then the preferred 

method would be to use the multinomial logit to model the predictors of mode choice. 

Second, if individual level data on travel modes is not available (the usual circumstances 

in most police department), then an approximation to a utility function can be made. The 

approach, in this case, is to estimate a relative accessibility function and then apply that function 

to the predicted trip distribution. The relative accessibility function is a mathematical 

approximation to a utility function, rather than a measured utility function by itself. Because 

the cost components cannot be measured, at least for offenders, we use an inductive approach. 

Reasonable assumptions are made and mathematical functions are found that fit these 

assumptions. 

Relative Accessibility 

The relative accessibility approach produces a plausible model, not an analytical one. 

The plausibility comes by making reasonable assumptions about actual travel behavior. One can 

assume that walking trips will occur for short trips, say under two miles. Bicycle trips, on the 

29.11 
other hand, could occur over longer distances, but will still be relatively short (also, there is 

always the risk of traffic on the safety of bicycle trips). Transit trips (bus and train) will be used 

for moderately long distances but require an actual transit network. Finally, driving trips are 

the most flexible because they can occur over any size distance and road network. They are 

less likely to be used for very short trips, on the other hand, due to reasons discussed above. 

Hierarchical Approach to Estimating Mode Accessibility 

Using this approach, specific steps can be defined to produce a plausible accessibility 

model. The Mode Split tab in CrimeStat allows the estimation of a relative accessibility model. 

Figure 29.1 shows the setup page for the mode split module. There are four tabs in the mode 

split module: 

1. The setup tab

2. Calibrate mode split: I

3. Calibrate mode split: II

4. Calibrate mode split: III

The setup tab defines the three files that are used in the module. There is predicted 

origin file, a predicted destination file, and a predicted origin-destination trip file. Both the 

predicted origin file and the predicted destination file have to be defined as the Primary file or 

Secondary file and ID fields have to be defined for each. The predicted origin-destination trip 

file is input separately on this tab. The variable identifying the predicted number of trips must be 

defined. Figure 29.1 illustrates this one data set. 

The next three tabs define up to five separate travel modes. The first Calibrate mode split 

tab defines modes 1 and 2. The second tab defines modes 3 and 4 and the third defines mode 5. 

The user can assign any one mode to each of these available slots. For example, mode 1 could be 

walking; mode 2 could be driving; mode 3 could be bus; mode 4 could be train; and mode 5 

could be bicycle. There is no particular order to the assignment and not all five available modes 

have to be used. Figure 29.2 illustrates the defining of two modes on the ‘Calibrate mode split: I’ 

tab where mode 1 is walking and mode 2 is by bicycle. 

For each of the modes that are used, the user must define an impedance function. The 

impedance function is a mathematical function that approximates the discounting of that mode 

with distance. The user can use a pre-defined mathematical function or else estimate it using an 

already-calibrated impedance function (see Chapter 28, page 28.24). In the example in Figure 

29.12 
Figure 29.1: 

Mode Split Module 
Figure 29.2: 

The ‘Calibrate Model Split: I’ Tab 
 

 

29.2, a mathematical function is being used for both the walking mode (mode 1) and the bicycle 

mode (mode 2). For the walking mode, the following negative exponential function is used: 

while bicycle mode uses a negative exponential function of the following form: 

 (29.13) 

 (29.14) 

Note that these are not probabilities but frequencies. That is, equation 29.13 estimates the 

number of walking trips as a function of distance between two zones, ݅ and ݆. Each equation 

discounts the likelihood of using that mode as a function of distance between the zones. The 

probabilities are estimated later when the frequency of all modes have been defined. 

Spreadsheet for Estimating Mode Split Impedance Values 

To identify the parameters that produce a plausible model of mode frequency, an Excel 

spreadsheet has been developed for making these calculations (Mode Split Impedance 

Defaults.xls). It is part of the “Crime Travel Demand Sample Data.zip” file and can be 

downloaded from the CrimeStat download page. Figure 29.3 shows part of the spreadsheet. 

The spreadsheet has been defined with distance, but it can be adapted for travel time or 

travel cost as well. A spreadsheet has been used because it is more flexible than incorporating it 

as a routine in CrimeStat to estimate the parameters. There is not a single solution to the 

parameters estimates and the different choices can be seen more easily. 

Define travel modes 

The following provides instructions on estimating the impedance values with the 

spreadsheet. In the CrimeStat mode split routine, up to five different modes are allowed. First, 

the user should define the travel modes to be modeled. In the spreadsheet, these have default 

names of AWalk@, ABike@, ADrive@, ABus@, and ATrain@. The user is not required to use these 

names nor all five modes. Clearly, if there is not a train system in the study area, then the 

ATrain@ mode does not apply. Travel modelers use variations on these, such as Adrive 

alone@,@carpool@, Aautomobile@, Amotorcycle@, and so forth. 

29.15 
Figure 29.3: 

EstimatingMode Split Impedance Values 
 

 

 

 

 

 

Define target proportions 

Second, define the target proportions. These are the expected proportions of travel for 

each mode. Where would such proportions come from? There have been many studies of 

driving and transit behavior, but relatively few studies of bicycle and pedestrian use (Schwartz et 

al, 1999; Porter, Suhrbier & Schwartz, 1999; Turner, Shunk, & Hottenstein, 1998). There are not 

simple tables that one can look up default values. 

Other studies 

To solve this problem, examples were sought from different size metropolitan areas. 

Estimates of travel mode share for all trip purposes (work and non-work) were obtained from 

Ottawa (McCormick Rankin, 2011; Ottawa, 2008), Portland (Portland, 1998); and Houston3. 

Table 29.1 shows the estimated shares. The Houston data does not include walking and biking 

shares, and transit trips are not distinguished by mode in the Portland and Ottawa data. 

Table 29.1: 

Estimated Mode Share for Three Metropolitan Areas 

All Trip Purposes 

Ottawa Portland Houston 

Population: 725 thousand 2.0 million 4.6 million

 (1995) (2001) (2000) 

Percent of trips by: (1995) (1994) (2025 forecast) 

Driving 73.5% 88.6% 98.3% 

Transit 15.2% 3.0 1.7% 

(bus 1.1%; rail 0.6%) 

Walking 9.6% 4.6% -

Bicycle 1.7% 1.0% -

Other - 2.8% -

While it is difficult to generalize, walking is very much dependent on both the 

compactness of the city and the existence of an extensive transit system. In Houston, the transit 

system is primarily a commuter system whereas in Portland and Ottawa, it serves multiple 

purposes. Clearly, the more compact is the urban area, the more likely that trips will occur by 

transit, walking or biking. But, even in the case of Ottawa where almost 10% of trips are by 

Houston-Galveston Area Council. Personal communication. 2004. 

29.17 

3 
 

 

walking, the majority of trips are by private vehicle. In the United States and Canada, for 

metropolitan areas with extensive transit facilities (New York, Chicago, Boston, Montreal), a 

majority of regional trips are still by automobile. 

Based on this, some default values were selected and put into the spreadsheet. The 

spreadsheet requires that they are entered as proportions (not percentages). The defaults values 

were (Table 29.2): 

The user can modify these in the spreadsheet. It is important that a user contact the 

local Metropolitan Planning Organization to find out what would be reasonable values for the 

urban area. The default values are guesses based on a limited amount of data. 

Table 29.2: 

Default Mode Share Values 

Proportions 

Mode Share 

Driving .90 

Bus .04 

Train .01 

Walk .04 

Bicycle .01 

Journey to work census 

An alternative approach is to use the Journey to Work data of the U.S. Census Bureau 

(2004). During every census, the Census Bureau documents home-to-work >commute= trips and 

breaks down these data by mode share. They release these data under the title AJourney to Work@. 

The 2010 Journey to Work data set has not been released. However, in 2000 in the United 

States, 87.9% of all home-to-work trips were by private vehicle (automobile, van, truck), 4.7% 

were by public transit (bus 2.5%; rail 2.1%; other 0.1%), 2.9% were by walking, 0.4% were by 

bicycle, 0.1% were by motorcycle, 0.7% were by other means, and 3.3% worked at home. 

National journey to work statistics for 1990 and 2000 and for metropolitan areas in 1990 

can be found at U.S. Census, 2009). Data on metropolitan areas for 2000 can be found in 

McGuckin and Srinivasan (2003). In 2000, the home-to-work mode share for a sample of large 

metropolitan (including the 15 largest) areas is shown in Table 29.3. They are rank-ordered by 

the 2000 population of the metropolitan area. 

As can be seen, the larger metropolitan areas generally have a higher share of transit use 

and walking than smaller metropolitan areas, but the differences are not that dramatic. Further, 

29.18 
 

 

 

Table 29.3: 

Mode Share of Journey to Work Trips: 2000 

(From McGuckin & Srinivasan, 2003) 

Greater Mode Share 

Metropolitan 2000 

Area Pop (M) Walk Bicycle Drive Bus Rail Other* 

New York 21.1 5.6% 0.3% 65.7% 6.8% 17.1% 4.5% 

Los Angeles 16.4 2.6% 0.6% 87.6% 4.3% 0.3% 4.6% 

Chicago 9.2 3.1% 0.3% 81.5% 4.6% 6.6% 3.9% 

Washington DC 7.6 3.0% 0.3% 83.2% 4.1% 5.0% 4.4% 

San Francisco 7.0 3.3% 1.1% 81.0% 5.7% 3.5% 5.4% 

Philadelphia 6.2 3.9% 0.3% 83.6% 5.3% 3.3% 3.6% 

Detroit 5.5 1.8% 0.2% 93.4% 1.7% 0.0% 2.9% 

Boston 5.8 4.1% 0.4% 82.7% 3.2% 5.5% 4.1% 

Dallas 5.2 1.5% 0.1% 92.7% 1.6% 0.1% 4.0% 

Houston 4.7 1.6% 0.3% 91.3% 3.1% 0.0% 3.7% 

Atlanta 4.1 1.3% 0.1% 90.6% 2.4% 1.1% 4.5% 

Miami 3.9 1.8% 0.5% 90.1% 3.2% 0.5% 3.9% 

Seattle 3.6 3.2% 0.6% 84.4% 6.2% 0.0% 5.6% 

Phoenix 3.3 2.1% 0.9% 90.0% 1.9% 0.0% 5.1% 

Minneapolis/ 

St Paul 3.0 2.4% 0.4% 88.4% 4.4% 0.0% 4.4% 

Cleveland 2.9 2.1% 0.2% 91.1% 3.1% 0.3% 3.2% 

San Diego 2.8 3.4% 0.6% 86.9% 3.1% 0.2% 5.8% 

St Louis 2.6 1.6% 0.1% 92.5% 2.1% 0.2% 3.5% 

Denver 2.6 2.4% 0.7% 87.1% 4.2% 0.1% 5.5% 

Pittsburgh 2.4 3.6% 0.1% 87.1% 6.0% 0.1% 3.1% 

Portland 2.3 3.0% 0.8% 85.2% 5.1% 0.5% 5.4% 

Cincinnati 2.0 2.3% 0.1% 91.4% 2.8% 0.0% 3.4% 

Sacramento 1.8 2.2% 1.4% 88.9% 2.4% 0.3% 4.8% 

Kansas City 1.8 1.4% 0.1% 93.2% 1.2% 0.0% 4.1% 

Milwaukee 1.7 2.8% 0.2% 90.0% 3.9% 0.0% 3.1% 

Indianapolis 1.6 1.7% 0.2% 93.3% 1.2% 0.0% 3.6% 

Orlando 1.6 1.3% 0.4% 92.7% 1.6% 0.0% 4.0% 

San Antonio 1.6 2.4% 0.1% 90.9% 2.8% 0.0% 3.8% 

Norfolk 1.6 2.7% 0.3% 91.0% 1.7% 0.0% 4.3% 

Las Vegas 1.6 2.4% 0.5% 89.5% 3.9% 0.0% 3.7% 

Charlotte 1.5 1.2% 0.1% 93.8% 1.3% 0.0% 3.6% 

29.19 
 

 

 

 

--------------------------------------------------------------------------------------------------------------------- 

Table 29.3: (continued) 

Greater Mode Share 

Metropolitan 2000 

Area Pop (M) Walk Bicycle Drive Bus Rail Other* 

New Orleans 1.3 2.7% 0.6% 87.7% 5.2% 3.8% 

Salt Lake City 1.8% 0.4% 90.3% 2.7% 0.3% 

0.0%

4.5% 

Memphis 1.1 1.3% 0.1% 93.9% 1.6% 0.0% 3.1%

Rochester 

1.3 

3.5% 0.2% 90.9% 1.9% 0.0% 3.5%

Oklahoma City 1.1

1.1 

1.7% 0.2% 93.8% 0.5% 0.0% 3.8% 

Louisville 1.0 1.7% 0.2% 92.9% 2.2% 0.0% 3.0%

* Includes taxi, ferry, and working at home

for even the largest metropolitan areas, the majority of the home-to-work trips are by private 

vehicle. 

The problem with these data, however, is that they only examine work trips. Nationally, 

home-to-work trips represent only about 15% of all daily trips (BTS, 2002). On the other hand, 

45% of daily trips are for shopping and errands and 27% are social and recreational. Further, 

non-work trips are even more likely to occur by automobile, and are generally shorter. For 

example, in Houston, for home-based non-work trips, only 1% of trips were by transit compared 

to 3.1% for home-to-work trips in 2004. These home-based non-work trips may be a better 

analogy to crime trips than work trips since they tend to be of similar trips lengths as crime trips. 

Thus, unless the user is willing to assume that a crime trip is like a work trip (which is 

questionable), then the Journey to Work tables are probably not the best guide for the target 

proportions. Nevertheless, an examination of them is valuable to see how work trips are split 

among the various travel modes. 

Select mode functions 

Third, select mathematical functions that approximate accessibility utility. Again, some 

plausible assumptions need to be made. In CrimeStat, the user can select among five different 

mathematical functions (linear, negative exponential, normal, lognormal, truncated negative 

exponential). The default functions are shown in Table 29.4 below. 

29.20 
 

 

 

 

 

Table 29.4: 

Default Mode Share Functions 

Mode Function 

Walk Negative exponential

Bicycle Negative exponential

Driving Lognormal

Bus Lognormal

Train Lognormal

The reasoning behind this is that walking and biking are relatively short trips whereas 

transit modes involve intermediate length trips. Finally, driving can be used for any length trip 

other than very short trips (e.g., less than one or two blocks). Thus, it is unlikely that an 

automobile will be used for very short trips (less than a quarter mile) and it is very unlikely that 

transit will be used for short trips (less than a half mile or more). Nevertheless, the user can 

modify these choices and examine the appropriate column in the spreadsheet. 

Select model priorities 

Fourth, select the priorities for modeling the target. Unfortunately, there may not be a 

single solution that will yield the target proportions. Therefore, a decision needs to be made on 

which order the spreadsheet will be calculated. The default order is shown Table 29.5. 

Table 29.5: 

Default Mode Share Functions 

Order of 

Mode Iteration 

Walk 1

Bicycle 2

Driving 3

Bus 4

Train 5

The reasoning is that the offender first makes a decision on the length of the trip (short, 

medium, long, or the equivalent in travel time). Then, within each category, the offender makes a 

decision on which mode to choose. For very short trips, the default mode is walking. For 

intermediate to long trips, the default choice is driving. However, the user can change this 

order. 

29.21 
 

Iteratively estimate parameters 

Fifth, in the spreadsheet, iteratively adjust the parameters until the target proportion is 

reached. Do this in the order selected in the above step. Again, there is not a single solution 

that will produce the target proportion. For example, each of the mathematical functions has two 

or three parameters that can be adjusted: 

1. For the negative exponential, the coefficient and exponent

2. For the normal distribution, the mean distance, standard deviation and coefficient

3. For lognormal distribution, the mean distance, standard deviation and coefficient

4. For the linear distribution, an intercept and slope

5. For the truncated negative exponential, a peak distance, peak likelihood, intercept,

and exponent.

The target proportion can be achieved by adjusting any or all of the parameters. For 

example, to achieve a target proportion of 0.05 (i.e., 5%) using the negative exponential, an 

infinite number of models can yield this, for example coefficient=0.0366, exponent=-2.63; 

coefficient=0.0459 or exponent=-5; coefficient=0.01966, exponent=-1; and so forth. Therefore, 

there must be additional criteria to constrain the choices. 

One criterion is to set an approximate mean distance. For example, with walking trips, 

the mean distance can be set to a half mile or for driving, the mean distance can be set to 6 miles. 

Then, check the approximate mean distance of the selected function. Though rarely will the exact 

mean distance be replicated, the calculated mean distance should be close to the ideal. The one 

exception is for very short trips. Since the intervals in the spreadsheet are a half mile each, 

there is considerable error for very short distances. 

Examine the graphs in the spreadsheet 

Another diagnostic tool is to examine the graph of the function in the spreadsheet (below 

the calculations). Does the typical trip approximate the expected mean distance? Does the 

selected function produce something that looks intuitive? Admittedly, these are subjective 

decisions. But, if the function looks strange, it can be caught and re-calculated. 

In short, the aim should be to produce a function that not only captures the target 

proportion, but looks plausible. Several examples are shown below. Figure 29.4 shows the 

default walking model using a negative exponential. Figure 29.5 shows the default biking 

model, also using a negative exponential. Figure 29.6 shows the default driving mode using a 

lognormal function. Figure 29.7 shows the default bus mode, also using a lognormal function, 

and Figure 29.8 shows the default train mode using a lognormal function. 

29.22 
Figure 29.4: 

Negative Exponential Function: Walk Mod

0.0200.0150.010

ance proportion 

0.005

Imped 

0.000

0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 2

Distance (miles)
Figure 29.5: 

Negative Exponential Function: Bike Mode 

0.002

ion 

0.001

pedance proporti Imp 

0.000

0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 2

Distance (miles)
Figure 29.6: 

Lognormal Function: Drive Mode 

0.070.050.06

on 

0.030.04

pedance proporti 

0.010.02

Imp 

0.00

0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 

Distance (miles)
Figure 29.7: 

Lognormal Function: Bus Mode 

0.0050.004

on 

0.0020.003

edance proportio 

0.001

Imp 

0.000

0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 2

Distance (miles)
Figure 29.8: 

Truncated Negative Exponential Function: Train 

0.002

tion 

0.001

pedance proport Imp 

0.000

0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 2

Distance (miles)
 

Figure 29.9 shows the cumulative results of the default values. This is also graphed in 

the spreadsheet, starting in cell I1. Notice how the relative accessibility function works. As 

distance increases, the mode proportions change. At very short distances, walking trips 

predominate with biking trips also getting a moderate share. As the distance increases, the 

proportions increasingly shift toward driving. Even though the likelihood of driving declines 

with distance, the other modes decline even faster. In other words, the relative accessibility 

function is estimating the relative shares of each mode as a function of the impedance (in this 

case, distance). Note also the relative differences in the frequency of trips. Driving trips are far 

more frequent than any other mode. Thus, compared to the individual graphs (figure 

29.4-29.8), the other modes are more muted than driving. 

Adapting spreadsheet for travel time or travel cost 

The illustrations to this point have used distance as an impedance unit. However, other 

impedance units, such as travel time and generalized travel cost, can also be used. These 

generally require a network (see below) in that weights have to be assigned to segments. 

Nevertheless, the same logic applies. For each travel mode, a specific impedance function is 

estimated and then applied to the trip distribution matrix. 

Empirically Estimating the Mode-specific Impedance 

As mentioned at the beginning of this chapter, the lack of information about offender 

travel modes has necessitated the use of mathematical >guesses= about travel behavior. 

However, if it were possible to obtain actual information on travel modes by offenders, then this 

information could be utilized directly to estimate a much more accurate impedance function. 

The multinomial logit model in the Discrete Choice module could be used for this purpose (see 

Chapters 21 and 22). The advantage would be enormous. Instead of guesses about likely 

impedance functions of specific travel modes, the user would have a function that was based on 

real data. There should be a substantial improvement in modeling accuracy. However, these 

data have to be first collected. 

CrimeStat IV Mode Split Tools 

The CrimeStat mode split module allows the relative accessibility function to be 

calculated. The following provides detailed instructions on running the model. Figure 29.2 

above showed the setup page for the mode split routine and Figure 29.3 showed the setup for 

modes 1 and 2, in the example AWalk@ and ABicycle@. The setup for modes 3, 4, and 5 are 

similar. 

29.28 
Figure 29.9: 

Relative Accessibility y b Mode 

0.050.04

n 

0.020.03

dance proportion 

0.01

Imped 

0.00

Distance (miles)
 

Mode Split Setup 

On the mode split setup page, the predicted origin and predicted destination files must be 

input as the primary and secondary files. If the origin and destination files are identical (i.e., all 

the origin zones are included in the destination zones), then the file must be input as the primary 

file. 

In addition, the user must input a predicted origin-destination trip file from the trip 

distribution module. Finally, an assumed impedance value for trips from the AExternal zone@

must be specified. The default is 25 miles. Choose a value that would represent a >typical= trip 

from outside the study region. 

For each mode, the user must provide a label for the name and define the mathematical 

function which is to be applied and specify the parameters. The first time the routine is opened, 

the default values are listed. However, the user can change these. 

Hint: Once the parameters are entered, they can be saved on the Options 

page. Then, they can be re-entered by loading the saved parameters file. 

Constrain Transit Trips to Network 

The impedance will be calculated either directly or is constrained to a network. The 

default impedance is defined with the type of distance measurement specified on the 

Measurement Parameters page (under Data setup). On the other hand, if the impedance is to be 

constrained to a network, then the network has to be defined. 

Default 

The default impedance is that specified on the Measurement parameters page. If direct 

distance is the default distance (on the measurement parameters page), then all impedances are 

calculated as a direct distance. If indirect distance is the default, then all impedances are 

calculated as indirect (Manhattan) distance. If network distance is the default, then all 

impedances are calculated using the specified network and its parameters; travel impedance will 

automatically be constrained to the network under this condition. 

Constrain to network 

An impedance calculation should be constrained to a network when there are limited 

choices. For example, a bus trip requires a bus route; if a particular zone is not near an existing 

bus route, then a direct distance calculation will be misleading since it will probably 

29.30 
 

 

underestimate true distance. Similarly, for a train trip, there needs to be an existing train route. 

Otherwise, the routine will assign transit trips where those are not possible (i.e., it will assign 

train trips where there are no train stations and it will assign bus trips where there are no bus 

routes). The routine does not >know= whether there are transit routes and must be told where 

they are. Even for walking, bicycling and driving trips, an existing network might produce a 

more realistic travel impedance than simply assuming a direct travel path. 

If the impedance calculation is to be constrained to a network, then the network must be 

defined. A more extensive discussion of a network is provided in Chapter 3 (under Type of 

distance measurement on the Measurement Parameters page) and in Chapter 30 in the discussion 

of the Trip Assignment module. Essentially, a network is a series of connected segments that 

specify possible routes. Each segment has two end nodes (in CrimeStat, they are called 

>FromNode= and AToNode). Depending on the type of network, the segments can be

bi-directional (i.e., travel is allowed in either direction) or single directional (i.e., travel is

allowed only from the AFromNode@ to the AToNode@).

A critical component of a network for the mode split routine is that travel can only pass 

through nodes. This means that two segments that are connected can allow a trip to pass over 

those two segments whereas two segments that are not connected cannot allow a trip to pass 

directly from one to the other. From outside the network, a trip connects to it at a node. For a 

transit network, this can be critical. For a bus route, it may or may not be important. A 

precise bus network defines nodes by bus stops so that a trip can >enter= or >leave= the bus system 

at a real stop. A less precise bus network defines nodes by the ends of segments (e.g., the end 

nodes of a TIGER segment). The routine will not know whether the node it enters or leaves from 

is a real bus stop or not. In the case of bus routes, it probably does not matter since they generally 

make very regular stops (every two or three blocks). 

Accurately defined transit networks 

For train networks, however, it is absolutely critical that the network be defined 

accurately. The nodes must be legitimate stations; a trip can only enter or leave the train system 

through a station (i.e., it cannot enter or leave a train network at the end of an arbitrary segment 

node). Most travel demand models use very precise bus and train networks that have been 

carefully checked; where errors occur, the networks are edited and updated. 

Utility for creating transit network 

If the user does not have an edited transit network, one can be made in the trip 

assignment module. There is a ACreate a transit network from primary file@ routine that will 

draw segments between input primary file points; the user inputs the station or bus stop locations 

29.31 
 

as the primary file and the routine creates a network from one point to the next in the same order 

as in the primary file (i.e., the primary file needs to be properly sorted in order to travel). See 

Chapter 30 for more information about creating a transit network. 

Entering the network parameters 

The network is input by selecting AConstrain to network@ and click on the >Parameters=

button. A dialogue is brought up that allows the user to specify the network to be used. The 

network file can be either a shape line or polyline file (the default) or another file, either dBase 

IV >dbf=, Microsoft Excel ‘xls/xlsx’,Microsoft Access >mdb=, Ascii >dat=, or an ODBC-compliant 

file. If the file is a shape file, the routine will know the locations of the nodes. All the user 

needs to do is identify a weighting variable, if used, and possible one way routes (>flags=). For a 

dBase IV or other file, the X and Y coordinate variables of the end nodes must be defined. These 

are called the AFrom@ node and the AEnd@ node, though there is no particular order. 

An optional weight variable is allowed for both a shape or dbf file. The routine identifies 

nodes and segments and finds the shortest path. By default, the shortest path is in terms of 

distance though each segment can be weighted by travel time, travel speed, or generalized cost; 

in the latter case, the units are minutes, hours, or unspecified cost units. 

Finally, the number of graph segments to be calculated is defined as the network limit. 

The default is 50,000 segments. This can be changed, but be sure that this number is greater 

than the number of segments in your network. 

Minimum absolute impedance 

If a mode is constrained to a network, an additional constraint is needed to ensure 

realistic allocations of trips. This is the minimum absolute impedance between zones. The 

default is 2 miles. For any zone pair that is closer together than the minimum specified (in 

distance, time interval, or cost), no trips will be allocated to that mode. This constraint is to 

prevent unrealistic transit trips being assigned to intra-zonal trips or trips between nearby zones. 

CrimeStat uses three impedance components for a constrained network: 

1. The impedance from the origin zone to the nearest node on the network (e.g.,

nearest rail station);

2. The impedance along the network to the node nearest to the destination; and

3. The impedance from that node to the destination zone.

29.32 
Since most impedance functions for a mode constrained to a network will have the 

highest likelihood some distance from the origin, it=s possible that the mode would be assigned 

to, essentially, very short trips (e.g., the distance from an origin zone to a rail network and then 

back again might be modeled as a high likelihood of a train trip even though such a trip is very 

unlikely). 

For each mode that is constrained to a network, specify the minimum absolute 

impedance. The units will be the same as that specified by the measurement units. The default is 

2 miles. If the units are distance, then trips will only be allocated to those zone pairs that are 

equal to or greater in distance than the minimum specified. If the units are travel time or speed, 

then trips will only be allocated to those zone pairs that are farther apart than the distance that 

would be traveled in that time at 30 miles per hour. If the units are cost, then the routine 

calculates the average cost per mile along the network and only allocates trips to those zone pairs 

that are farther apart than the distance that would be traveled at that average cost. 

Applying the Relative Accessibility Function 

To apply the relative accessibility function, the parameter choices for each mode are 

entered into the mode split routine. All transit modes are then constrained 

Once the mode split setup has been defined and all transit modes have been constrained to a 

proper network, the mode split routine can be run. 

Figure 29.10 shows the top 300 walking crime trips in Baltimore County estimated with 

the default accessibility functions. As seen, the vast majority of walking trips are intra-zonal 

(local). There are only a couple of inter-zonal walking trip links shown. The default 

impedance function assigned approximately 4% of the trips to this mode and the result is many 

intra-zonal trips. 

Figure 29.11 shows the top 300 bicycle crime trips in Baltimore County. There are fewer 

trips by bicycle and they also tend to be quite local. The impedance function used for bicycle 

trips allocated approximately 1% of all trips to this mode. Thus, it=s less frequent than walking 

mode. There are proportionately more inter-zonal trips among the top 300 than for walking 

trips, but these tend to be quite short (travel between adjacent zones). 

On the other hand, driving is the predominant travel mode for the crime trips (Figure 

29.12). The impedance function used allocated approximately 90% of the trips to driving. 

Among the top 300 links, there were no intra-zonal driving trips. The use of a lognormal function 

minimized intra-zonal travel. 

29.33 
Figure 29.10: Figure 29.10: 
Figure 29.11: Figure 29.11: 
Figure 29.12: 
 

 

To allocate bus and train trips, however, it was necessary to constrain them to a network. 

Separate bus and train networks were obtained from the Baltimore Metropolitan Council. 

Figure 29.13 shows the Baltimore bus network and Figure 29.14 shows the predicted bus trips 

superimposed over the bus network. Overall, about 4% of the total trips were allocated to the bus 

mode by the accessibility function. As seen, the trips tend to be moderate distances and tend to 

be close to the bus network. Constraining these trips by the network decreased the likelihood that 

the routine would assign a particular trip link that was far from the bus work to a bus trip. 

Finally, train crime trips were constrained to the train network. Figure 29.15 

superimposes the assigned train trips over the intra-urban rail network. Overall, only 1% of the 

total trips were allocated to train mode. Therefore, the number of trips for any zone pair is quite 

small. The trips are generally longer than the bus trips, as might be expected, and they also 

tend to fall along the major rail lines. Some of the trips start quite far from the rail lines, so it=s 

possible that these are not realistic representations. Keep in mind that this is a mathematical 

model and is far from perfect. 

Overall, the mode split routine has produced a reasonable approximation to travel modes 

for crime trips. Since there was no data upon which to calibrate the functions, reasonable 

guesses were made about the accessibility function. The mathematical model produced a 

plausible representation of these assumptions, generally fitting into what we know about crime 

travel patterns. 

Usefulness of Mode Split Modeling of Crime Trips 

The mode split model is a logical extension of the travel demand framework. For 

transportation planning, it is an important step in the process. But, it also is important for crime 

analysis. First, it addresses the complexity of travel by separating the trips from specific origins 

to specific destinations into distinct modes. In this sense, it adds more realism to our 

understanding of criminal travel behavior. The Journey-to-crime literature, which has been used 

by crime analysts and criminal justice researchers to Aunderstand@ criminal travel behavior, is 

simplistic in this respect. It assumes a single mode, though that is rarely articulated by the 

researchers. By pointing out typical travel distances by offenders circumvents the critical 

question of how they made the trip. This was, perhaps, not as critical 50-60 years ago when 

most crimes were committed within a smaller community and it could be assumed that most 

offenders walked to the crime location. But in the post- World War era, automobile travel has 

become increasingly dominate. This model assumes that the vast majority of crime trips are 

taken by automobile. While there is currently no data to prove that assertion, it follows from 

the transportation patterns that have become widespread in the U.S. and elsewhere. 

29.37 
Figure 29.13: 
Figure 29.14: Figure 29.14: 
Figure 29.15: 
There is a second reason why an analysis of crime travel mode can be important. If the 

limitations of travel mode information could be improved through better and more careful data 

collection by police and other law enforcement agencies, this type of analysis could be very 

useful for policing. For one thing, it could allow more focused police deployment. For 

neighborhoods with a predominance of walking crime trips, then a police foot patrol could be 

most effective. Conversely, for neighborhoods with a predominance of driving crime trips, then 

patrol cars are probably the most effective. Police intuitively understand these characteristics, but 

the crime mode split model makes this more explicit. 

For another thing, a mode split analysis of crime can better help crime prevention efforts. 

As the Baltimore data suggest, many of the local (intra-zonal) crime trips are committed around 

housing projects and in very low income communities. Most likely, this is a by-product of 

poverty, lack of local employment opportunities, deteriorated housing, and even poor 

surveillance. Since teenagers are more likely to not own vehicles, it might be expected that the 

majority of these local crime trips are committed by very young offenders (see Levine & Lee, 

2012). This can be useful in crime prevention. Again, AWeed and Seed@ and after-school 

programs are generally targeted to youth from very low income neighborhoods. What is shown 

by the mode split analysis is probably the crime patterns associated with these neighborhoods. 

Even though it is intuitively understood, the mode split analysis quantifies these relationships in 

an explicit manner. 

In short, a mode split analysis of crime trips is an important tool for crime analysts and 

criminal justice researchers. If correctly calibrated, it can help focus police enforcement and 

crime prevention efforts more specifically and can improve the theory of criminal travel 

behavior. 

Hopefully, police departments will start to improve the quality of data in capturing likely 

travel modes while taking incident reports. Even though most police departments have an item 

similar to AMethod of departure@, there has not been a lot of emphasis on this information and 

most crime data sets are deficient on this information. However, with improved data will come 

more accurate accessibility functions and, hopefully, even real utility functions where actual 

costs are measured. The expectation is that this will happen and we should work towards 

accelerating the process. 

Limitations to the Mode Split Methodology for Crime Analysis 

There are also limitations to the method, particularly the aggregate approach. First, the 

aggregate approach does not consider individuals, only properties associated with zones (e.g., 

average travel time between two zones). As mentioned earlier, the accessibility function used 

29.41 
 

(or the underlying utility theory) is much simpler for zones than for individuals. Consequently, 

the analysis is cruder at an aggregate level than at an individual level. Policy scenarios are much 

more limited with aggregate mode split than with individual-level models. For example, if an 

analyst wanted to explore what was the likely effect of increased public surveillance on walking 

behavior by pickpockets, it is more difficult to do with aggregate data than with individual data. 

For example, it could be hypothesized that actual pickpockets are more sensitive to increased 

public surveillance than, say, car thieves, but this cannot be tested at the aggregate level. 

Instead, some general characteristics are assigned to all individuals (e.g., the number of security 

personnel in a zone). 

Second, the zonal model for mode split (as with trip distribution) cannot explain 

intra-zonal travel. For intra-zonal trips, it is inaccurate and generally defaults to simple choices 

(e.g., walking, biking or driving). For example, bus or train mode can rarely be applied at an 

intra-zonal level because there are usually too few network segments that traverse a zone and the 

segments rarely stop within the zone. While this deficiency also applies to the trip distribution 

model, the dependence on a network for transit modes, particularly, leads to underestimation of 

transit use for very short trips. 

Third, the zonal mode split model cannot explain individual differences. This goes back 

to the first point that a single utility function is being applied at the zonal level. Thus, the value 

of time to different individuals living in the same zone cannot be examined; instead, everyone is 

given the same value. 

Fourth, the aggregate mode split model does not analyze time of day very well. The 

probabilities are assigned to all trips, rather than to trips taken at particular times of the day. To 

conduct that analysis, an analyst has to break down crimes by time of day and model the 

different periods separately. Aside from being awkward, the summed trips need to be balanced 

to ensure that they sum to the total number of trips. 

Fifth, and finally, the mode split model, both aggregate and disaggregate, cannot explain 

linked trips (sometimes called trip chaining). An offender might leave home one day, go out to 

eat, visit a friend, commit a street robbery, go to a >fence= to distribute the goods, buy drugs from 

a drug dealer, and then finally go home. The mode split model treats each of these as separate 

trips; in the case of crime mode split, there are three distinct crime trips - committing the 

robbery, selling the stolen goods to the >fence=, and buying the drugs from the drug dealer. The 

model does not understand that these are related events, but instead assigns separate mode 

probabilities to each trip. Thus, it is possible to produce absurd choices, such as driving to the 

crime scene, taking the bus to the drug dealer, and then biking home. In this respect, the 

disaggregate approach is equally flawed as the aggregate since both treat each trip as 

independent events. The solution to this lies in a >third generation= of travel modeling in which 

individual trip makers are simulated over a day; activity-based modeling, as it is known, is still in 

29.42 
 

a research stage (FHWA, 2009; Culp & Lee, 2005; Miller & Salvini, 1999). But, it will 

eventually emerge as the dominant travel demand modeling algorithm. 

Conclusions 

Nevertheless, mode split modeling can be a very useful analysis step for crime analysis. 

It represents a new approach for crime analysis and one with many useful possibilities. It will 

require building more systematic databases in order to document travel modes. But, the 

possibilities that it offers up can be important for crime analysts and criminal justice researchers 

alike. 

29.43 
 

References 

Ben-Akiva, M. & Lerman, S. (1985). Discrete Choice Analysis: Theory and Application to 

Travel Demand. MIT Press: Cambridge. 

Bernasco, W. & Block, R. (2009). Where offenders choose to attack: A discrete choice model of 

robberies in Chicago. Criminology 47(1): 93-130. 

BTS (2002). National Household Travel Survey: Daily Travel Quick Facts. Bureau of 

Transportation Statistics, U.S. Department of Transportation: Washington, DC. 

http://nhts.ornl.gov/download.shtml#2009. Accessed June 1, 2012. 

Carnegie-Mellon University (1975). Security of Patrons on Urban Public Transportation 

Systems. Transportation Research Institute, Carnegie-Mellon University: Pittsburgh, PA. 

Johnson, M.A. (1978). Attribute importance in multiattribute transportation decisions, 

Transportation Research Record, 673, 15-21. 

Culp, M. & Lee, E. J. (2005). Improving travel models through peer review. Public Roads, 68 

(6), FHWA-HRT-05-005. Federal Highway Administration, U.S. Department of 

Transportation: Washington, DC. 

http://www.fhwa.dot.gov/publications/publicroads/05may/07.cfm. Accessed April 28, 2012. 

Domencich, T. & McFadden, D. (1975). Urban Travel Demand: A Behavioral Analysis. North 

Holland Publishing Company: Amsterdam & Oxford (republished in 1996). Also found at 

http://emlab.berkeley.edu/users/mcfadden/travel.html. Accessed April 28, 2012. 

FHWA (2009). Integrated Urban Systems Modeling, The Exploratory Advanced Research 

Program Fact Sheet, FHWA-HRT-09-042. Federal Highway Administration, U.S. Department 

of Transportation: Washington, DC. 

http://www.fhwa.dot.gov/advancedresearch/pubs/interurbsys.pdf. Accessed April 28, 2012. 

Levine, N. & Lee, P. (2009). Bayesian journey to crime modeling of juvenile and adult 

offenders by gender in Manchester. Journal of Investigative Psychology & Offender Profiling. 

6(3), 237-251. 

Levine, N. & Wachs, M. (1986). Bus Crime in Los Angeles: II - Victims and Public Impact. 

Transportation Research. 20 (4), 285-293. 

McCormick Rankin (2011). Transportation Demand Management Plan: Final Report. Ottawa. 

http://ottawa.ca/cs/groups/content/@webottawa/documents/pdf/mdaw/mdc3/~edisp/cap078202.p 

df. Accessed June 1, 2012. 

McFadden, D. L. (2002). The path to discrete-choice models. Access, No. 20, Spring. 20-25. 

http://www.uctc.net/access/access20.shtml. Accessed April 28, 2012. 

29.44 
References (continued)

McGuckin, N. A. & Srinivasan, N. (2003). Journey to Work in the United States and its Major 

Metropolitan Areas. FHWA-EP-03-058, Office of Planning, Federal Highway Administration: 

Washington, DC. 

Miller, E. J. & Salvini, P. A. (1999). Activity-based travel behavior modeling in a 

microsimulation framework. Paper presented at IATBR Conference, Austin, TX. December. 

http://www.civ.utoronto.ca/sect/traeng/ilute/downloads/conference_papers/miller-salvini_iatbr-9 

7.pdf. Accessed May 4, 2012.

Oppenheim, N. (1980). Applied Models in Urban and Regional Analysis. Prentice-Hall, Inc.: 

Englewood Cliffs, NJ. 

Ortuzar, J. D. & Willumsen, L. G. (2001). Modeling Transport (3rd edition). J. Wiley & Sons: 

New York. 

Ottawa (2008). Transportation Master Plan. Regional Municipality of Ottawa-Carleton. 

http://ottawa.ca/en/city_hall/planningprojectsreports/master_plans/tmp/. Accessed June 1, 2012. 

Porter, C., Suhrbier, J. & Schwartz, W. L. (1999). Forecasting bicycle and pedestrian travel: 

State of the practice and research needs. Transportation Research Record, 1674, 94-101. 

Portland (1998). Bicycle Master Plan. Resolution 35515, Office of Transportation, City of 

Portland: Portland, OR. 

http://www.portlandonline.com/transportation/index.cfm?a=369990&c=49304. Accessed June 

1, 2012. 

Roemer, F. & Sinha, K. (1974). Personal security in buses and its effects on ridership in 

Milwaukee, Transportation Research Record, 487, 13-25. 

Schnell, J. B., A. J. Smith, K. R. Dimsdale, & L. J. Thrasher (1973). Vandalism and Passenger 

Security: A Study of Crime and Vandalism on Urban Mass Transit Systems in the United States 

and Canada. Prepared by the American Transit Association for the Urban Mass Transportation 

Administration (now Federal Transit Administration), U. S. Department of Transportation. 

National Technical Information Service: Springfield, VA. PB 236-854. 

Schwartz, W.L., C. D. Porter, G.C. Payne, J.H. Suhrbier, P.C. Moe, & W.L. Wilkinson III 

(1999). Guidebook on Methods to Estimate Non-Motorized Travel: Overview of Methods. 

Turner-Fairbanks Highway Research Center, Federal Highway Administration: McLean, VA. 

July. http://www.fhwa.dot.gov/publications/research/safety/pedbike/98165/index.cfm. 

Accessed June 1, 2012. 

Stopher, P. R. & Meyburg, A. H. (1975). Urban Transportation Modeling and Planning. 

Lexington, MA: Lexington Books. 

29.45 
References (continued)

Turner, S., Shunk, G. & Hottenstein, A. M. (1998). Development of a Methodology to Estimate 

Bicycle and Pedestrian Travel Demand. Report 1723-S, Texas Transportation Institute: College 

Station. http://tti.tamu.edu/publications/catalog/record/?id=146. Accessed April 28, 2012. 

Train, K. (2009). Discrete Choice Methods with Simulation (2nd edition). Cambridge 

University Press: Cambridge. 

U.S. Census Bureau (2009). Commuting (Journey to Work) Main. U.S. Census Bureau, U.S. 

Department of Commerce: Washington, DC. http://www.census.gov/hhes/commuting/. 

Accessed June 1, 2012. 

WASHCOG (1974). Citizen Safety and Bus Transit. Metropolitan Washington Council of 

Governments. National Technical Information Service, Springfield, VA. PB 237-740/AS. 

29.46