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					   An Urban Commercial Vehicle Movement Model for Calgary

   KJ Stefan
   City of Calgary
   PO Box 2100, Stn. M, #8124
   Calgary, Alberta T2P 2M5
   Phone: 403-268-1630

   JDP McMillan
   City of Calgary
   PO Box 2100, Stn. M, #8124
   Calgary, Alberta T2P 2M5
   Phone: 403-268-5985

   JD Hunt
   University of Calgary
   2500 University Drive NW
   Calgary, Alberta T2N 1N4
   Phone: 403-220-8793

   Paper for presentation at the 84th Transportation Research Board Annual Conference

   Washington DC, January 2005

   Commercial vehicle movements comprise perhaps 15% of all urban vehicle trips, and produce even larger impacts
   in key areas such as congestion, emissions, road wear and industrial area traffic. A system for modelling commercial
   movements has been developed for Calgary in Canada. The resulting system of models includes the novel
   application of an agent-based microsimulation framework, using a tour-based approach and emphasizing important
   elements of urban commercial movement, including the role of service delivery, light commercial vehicles and trip
   chaining. The microsimulation uses Monte Carlo techniques to assign tour purpose, vehicle type, next stop purpose,
   next stop location, and next stop duration. Tours are ‘grown’ using a return-to-establishment alternative within the
   next stop purpose allocation, which is seen to be consistent with the nature of tour making in urban commercial
   movements. The Monte Carlo probabilities are established using a series of logit models, with coefficients estimated
   based on the observed behavior of different segments of commercial movements. The estimation results in
   themselves provide insights into the revealed behavior that have not been available previously.


TRB 2005 Annual Meeting CD-ROM                                                      Paper revised from original submittal.
            Commercial vehicle movements are a significant portion of travel in urban areas. It is estimated that about
   10 to 15 percent of urban vehicle trips are made for commercial purposes. But the impacts of commercial vehicles
   are even greater than this: commercial vehicles (and especially the larger trucks) tend to concentrate in industrial and
   commercial areas, and more during the middle of the workday than in the peaks. Additionally, when compared with
   the personal vehicle fleet, larger trucks can have more significant impacts per vehicle in key areas such as road
   congestion and traffic flow, greenhouse and pollutant emissions and pavement wear. Higher values of time are also
   typically attributed to commercial vehicles, so this sector should be considered separately in order to properly
   calculate the benefits of travel time savings.
            This paper uses ‘commercial’ in an inclusive sense, to mean not only trips made by commercial enterprises,
   but also by non-commercial organizations such as governments and government agencies, as well as trips by
   employees of charities and similar non-commercial organizations. A trip is considered to be ‘commercial’ if the trip
   maker is being reimbursed for making the trip above and beyond the reimbursement of travel costs. These trips are
   considered separate from the ‘personal’ trips produced by other elements of the overall Calgary model system.

            The Calgary census metropolitan region has a population of around one million. Calgary is a key hub for
   shipping in Western Canada, with key strategic highway and railway routes for commercial traffic. The City of
   Calgary has developed a regional travel model, focussing on personal travel patterns on a typical fall weekday.
   However, the treatment of commercial vehicle movements prior to the development of the Calgary Commercial
   Vehicle Model (CVM) was very limited, using a scaling of truck flows derived from count data. A limited treatment
   of commercial movements is typical of virtually all models of this sort, despite the importance of these movements
   as indicated above.


   Urban Commercial Vehicle Movement
            Commercial vehicle movement is very different from personal movement in many respects. Urban
   commercial movement is also different from commercial movement over larger areas, such as states, provinces,
   nations and international areas. Rail, ship and aircraft transport are inefficient over the smaller scale of urban areas,
   hence urban commercial movement is almost exclusively road-based (1). The bulk of the work in the field of
   commercial movement (or freight) modelling is done on a large regional scale, but importing these approaches to the
   urban context is inappropriate given the differences that exist.
            A large component of commercial movements within urban areas is made with light commercial vehicles
   (LCVs), including four-tire, two-axle vehicles such as pick-up trucks and vans and even passenger cars. Calgary
   data indicates that over 50% of urban commercial trips are made by light vehicles. Interurban transport makes load
   consolidation more economic, and the larger volumes of goods moving between larger areas (2) also increases the
   importance of heavy commercial vehicles such as tractor-trailer combinations over longer distances.

            Further, the service sector constitutes a much larger large proportion of the urban economy, in large part
   serving the population in the urban area where it is located. While almost all interurban transport is goods hauling,
   Calgary surveys revealed that approximately 50% of all business stops were made to provide a service.

            It is clear that a complete consideration of urban commercial movement requires consideration beyond just
   freight movements, expanding to include service deliveries with the urban area.

   Methods of Modeling Urban Commercial Vehicle Movement
             The most common methods for urban commercial vehicle modeling are various forms of expanded OD-
   matrix techniques, typically based on ground counts expanded to represent a steady-state and perhaps further scaled
   for future years (3). This general approach has several problems; the most obvious are the lack of policy response
   and the exclusion of the half of commercial movements that are made with light commercial vehicles. These models


TRB 2005 Annual Meeting CD-ROM                                                          Paper revised from original submittal.
   are perhaps only useful for providing ‘background traffic’, where personal vehicle modeling is the goal and
   commercial vehicles are of little interest beyond their consumption of roadway capacity.
             The second major methods use variations on the traditional four-step aggregate approach (4). Mode choice
   is often neglected entirely; usually only larger vehicles are considered. This approach emphasizes trips rather than
   tours, which misses the essential tour-based nature of urban commercial vehicle movements. Additionally, most
   models based on this approach tend to use simple ‘standard’ coefficients to handle tour generation and trip
   distribution, and often neglect service trips, focussing on goods movement alone.

            While most jurisdictions use one of the above-described modelling approaches for commercial vehicles (or
   neglect them entirely), a number of more novel approaches are being developed. The approach in supply chain
   modelling is to attempt representation of the suppliers, warehouses and consumers of products in individual supply
   chains (5). This would require substantial, hard-to-get data to produce a comprehensive model for a typically diverse
   North American urban area. Some models use the spatially disaggregate input-output approach, which does a good
   job of representing the commodity flows in the economy, but typically downplays or simplifies important elements
   of urban commercial movements, such as trip chaining and less than load hauling (6).

            The microsimulation approach has recently become the state-of-the-art in practice in household travel
   modelling (7). It is more flexible and powerful, but in general requires a richer dataset in order for these benefits to
   be realized effectively. Some of the related advantages of a microsimulation approach are the ability to model
   various aspects of choice behaviour explicitly, aggregate results in any manner desired, and the flexibility to create
   any number of specific constraints. The potential for a tour-based microsimulation model of urban truck / freight
   movements has been discussed in the literature (8) and some of the possible components of such a model partially
   explored empirically (9) but there do not appear to be any successful, practical implementations up to now.

   Calgary Commercial Vehicle Movement Model Design
             For the Calgary commercial vehicle movement model, a hybrid approach was developed with a tour-based
   microsimulation included. The total set of commercial vehicle movements was divided into three separately
   modelled groups: tour-based movements, fleet-allocator movements and external-internal movements. The majority
   of movements are covered by the tour-based model, with the two other movements being modelled with methods
   that use the available data.
             The external-internal movements model uses singly-constrained gravity models to assign the internal ends
   of trips being made by trucks crossing the model area boundary. This model only includes trucks – and excludes
   those commercial trips with at least one end external by light vehicles – because the external cordon survey done to
   collect the information used to develop this model only considered trucks; light vehicles were not stopped. These
   external movements comprise roughly 6% of all movements.

             Fleet allocators represent roughly one quarter of all commercial vehicle movements. Fleet allocators
   primarily dispatch vehicles to cover an area or to travel road links, rather than handle a specific shipment. Examples
   include newspaper delivery, garbage pickup, mail and courier service, taxi, police and rental car fleets. At this point
   the fleet allocator model uses an aggregate generation and gravity-style distribution framework.

             The remaining two thirds of trips are modelled using a tour-based microsimulation. This model identifies
   the attributes of individual tours, including stop purpose, vehicle type, time of day and the stop locations. It uses a
   Monte Carlo simulation with a series of logit models to simulate the decisions made with a touring vehicle. Because
   of the microsimulation element, results can be aggregated in any way to analyse tours for different industries, time
   periods or other results as needed.

             The tour-based microsimulator is the focus of this paper. Figure 1 illustrates the overall structure of the
   model. Tours are generated on a zonal level, then each tour is assigned a vehicle and overall tour purpose. A start
   time is then determined. The trips in the tour are then identified in an iterative process. For a given tour, only one
   vehicle type and one primary purpose are selected, but at least one and possibly more stop locations and purposes
   are selected, with the previous and current stops influencing the decisions on the next stop. To implement this, an
   iterative loop is formed with the next stop purpose being chosen, then the next stop location, then the duration of the
   stop at the destination.


TRB 2005 Annual Meeting CD-ROM                                                          Paper revised from original submittal.
             Tours are ‘grown’ in this implementation, rather than developed using the ‘rubber-banding’ approach
   commonly seen in personal travel modelling – where a primary destination is identified first and one or more further
   intermediate stops are identified out from the paths between the tour base and the primary destination after that (7),
   like stretching a rubber band first between the tour base and the primary destination and then possibly outwards on
   either side. This reflects the nature of commercial vehicle movements – rather than having a work or other
   compulsory purpose to anchor one end of the tour and perhaps one or two optional intervening stops, a range of
   equally important and possibly compulsory stops may be included and thus weigh on the nature of the trip. On every
   stop after the first, a next stop purpose decision is made. One of the alternatives in this decision, along with stopping
   at a client (business) and stopping at a non-client, such as a gas station or restaurant (other) is to make the decision
   to return to the establishment. A diagram of this is shown in figure 2. The iterative loop continues until the next stop
   purpose model determines that the vehicle is returning to the establishment, at which time the final trip is known and
   the tour is finished. The microsimulator then continues to the next generated tour.

            The primary source of data for this model is an extensive set of surveys of the shipments arising and
   associated vehicle movements made by business establishments – analogous to a household trip diary – collecting
   information on tours made in the Calgary region by just over 3,000 establishments on a typical weekday in 2001
   (10). Sampled establishments provided information on the movements of their entire fleet over a 24 hour period,
   including origin, destination, purpose, fleet and commodity information. Information on over 64,000 trips was
   available for the estimation and calibration of the tour based model.


   Model Fundamentals
            The Calgary CVM operates based upon three vehicle classes; light, medium and heavy. Light vehicles are
   passenger vehicles (cars, vans, pickup trucks), and operate over the entire road network. Medium vehicles are
   single-unit vehicles with six tires, and heavy vehicles are multi-unit vehicles with more than six tires. Both medium
   and heavy vehicles are subject to truck route restrictions in Calgary.

             Truck routes are explicitly modelled in the Calgary CVM; medium and heavy trucks are forced, through
   network penalties used in assignment, to travel only on truck routes unless making a stop that is inaccessible from
   truck routes. When travelling off of truck routes, they are to take the shortest possible path. Truck route travel paths
   are used in the calculation of travel times, generalised costs and accessibilities, and these were used throughout
   estimation. As a result, medium and heavy vehicles ‘see’ a different network from light vehicles, and truck route
   policies can be tested.

            A concept of purpose is also included in this model, much as ‘work’ and ‘school’ are included in a
   household personal travel model. These purposes represent different types of activities, with different influences
   and choice structures. The four purposes are goods, which includes handling goods; service, which is the
   performance of service (or goods handling to perform a service, such as a plumber picking up supplies); other,
   which comprises all non-business purposes; and return, which represents the return trip to the business
   establishment, either at the end of the day or during the day for any reason.

            Companies are segregated into 5 industry categories: private services (which includes government and
   education), retail, industrial (which includes agricultural), wholesaling and transportation. Each of these industry
   categories has unique coefficients throughout much of the model, so the model produces very different behaviours
   and reactions to policy changes for these different categories. The model also has a slightly different structure for
   ‘transportation’, most notably in that the goods and service stop and tour purposes are combined into a single
   business purpose, because transportation firms provide the service of handling goods, which blurs the definitions.

   Tour Generation Model
            The aggregate number of tours generated by each industry category is determined first for each time period
   in each model zone. These aggregate numbers of tours are used to form lists of discrete tours considered in the rest


TRB 2005 Annual Meeting CD-ROM                                                          Paper revised from original submittal.
   of the model. Tour generation rate (tours per employee in an industry) is determined using a regression with zonal
   attributes such as land use and accessibility included as independent variables. This rate is multiplied by the number
   of employees in the relevant industry in the zone to produce a total number of tours generated.
             One interesting result from the parameter estimations undertaken in the development of these regressions
   was the strong tendency for accessibility to have a negative constant. This differs from the expected positive value
   for models of personal travel. We believe this is due to differences in the motivations for commercial establishments
   and households. If congestion increases, a company’s vehicles will take longer to make their deliveries. At least in
   the short term (which is what the model covers) the company is still required to fulfil the needs of its clients, so it
   will have to add vehicles to the fleet and make more tours to be able to make the same number of deliveries.

   Tour Purpose and Vehicle Choice Model
             Each tour is assigned both a primary purpose and a vehicle type simultaneously. This is done using a Monte
   Carlo process where the selection probabilities are determined using single-level logit models based on industry type
   with utility functions that include land use, establishment location and accessibility attributes. The alternatives for
   the primary purpose are to (a) handle goods, (b) perform service visits or (c) undertake solely other non-business
   purposes. The alternatives for vehicle type are (a) light, (b) medium or (c) heavy. The need for a tour purpose model
   arose from earlier work, which revealed that virtually all tours make goods stops or service stops, but not a
   combination of both. Attempts to develop a nested model of these two choices resulted in nesting coefficients that
   were either unacceptable (greater than 1) or not significantly different from 1, indicating that the two decisions can
   be considered appropriately with a single-level structure.

              The generalized utility function for tour purpose and vehicle choice is as follows:

   Utour purpose, vehicle = ASCtour purpose, vehicle +
                             land use          +
                             land use          +
                             company            +
                             company           +
                            population accessibility
                                                       vehicle, land use
                                                                           × population accessibility for generating zone +
                            employment accessibility
                                                        vehicle, land use
                                                                            × employment accessibility for generating zone


                      ASCtour purpose, vehicle is the alternative specific constant for a given combination of tour purpose and
   vehicle choice (the constant for a goods tour by medium vehicle, for instance, is –5.393).
                       land use         and land usevehicle are constants for the vehicle choice and tour purpose, based on
   establishment zone land use. For instance, in an industrial land use zone, the constant for a goods tour by medium
   vehicle is adjusted by –0.1517 and +2.391, respectively.
                       company           and companyvehicle are similar to land usepurpose and land usevehicle, but based on the industry
   of the company generating the tour. For instance, for a wholesaling company, the constant for a goods tour by
   medium vehicle is now adjusted by +3.946 and +0.4309, respectively.
                                                vehicle, land use
                       population accessibility                   and employment accessibilityvehicle, land use are based on the land use of the
   establishment zone. For instance, for a medium vehicle in an industrial zone, the population accessibility is
   multiplied by 9.866×10-6.
            Coefficients for the above generalized function are shown in tables 1 through 3. Table 1 has the values for
   ASCtour purpose, vehicle, table 2 has the values for land usepurpose, land usevehicle, companypurpose and companyvehicle and table 3 has
   the values for population accessibilityvehicle, land use and employment accessibilityvehicle, land use .

            The tour purpose and vehicle choice model for the transportation industry is a reduction of the above
   generalized utility function. Transportation establishments were modelled with a single purpose tour, so only vehicle
   choice needs to be covered and the constants relating to tour purpose are not necessary. The resulting coefficients
   are shown separately for transportation establishments in table 4.


TRB 2005 Annual Meeting CD-ROM                                                                                Paper revised from original submittal.
            The first five terms of the utility function shown are simple constants. Figure 3 shows, using only the
   constants and neglecting accessibilities, the proportions of vehicle and tour purpose produced for a wholesaling
   company based in each of the land use categories. A wide variation can be seen in both vehicle type and tour
   purpose; for instance, there is a big difference in the use of light and medium vehicles for establishments based in
   residential versus commercial/retail land uses.

            Some ceritas paribus general trends indicated by the estimated values are notable. Overall, light vehicles
   are used more than medium and heavy vehicles. Retail companies are even more likely to use light vehicles,
   whereas industrial, wholesaling and transportation companies are more likely to use medium and heavy vehicles. As
   expected, companies based in residential areas are most likely to use light vehicles, with commercial/retail land use
   emphasizing medium vehicles more than the other land uses.

             Similarly, service is favored compared to goods, with other tours a relative rarity. Unsurprisingly, private
   service companies are more likely to make service tours than other companies, with wholesaling then retailing
   having the highest propensity to make goods tours. While industrial companies are more likely and the wholesaling
   less likely to make other tours, nobody makes a lot of them. Land use also plays a role in this process, with
   commercial/retail the most likely to produce service tours and low-density the most likely to produce goods tours.

              The accessibilities provide some feedback of network conditions into this model, although they have minor
   effect. It can be argued that this is true in reality – even very large changes in network congestion would be unlikely
   to bring about changes in vehicle choice for a flower delivery service or a gravel hauler.

   Tour Start Time Model

             Tours are first generated within five time periods, divided from the daily total using a single-level logit
   model. The Calgary CVM deals with exact time; each stop occurs at a specific time, so the model output can be
   aggregated in different time periods as necessary. The model uses a Monte Carlo process is used to assign an exact
   start time, with sampling distributions based on observed start times differentiated by industry and time period.

   Next Stop Purpose Model
            The purpose is selected for each subsequent stop, from the alternatives goods, service, other or return to
   establishment. If the primary purpose of the tour is to handle goods, then the service alternative is not available for
   stops on the tour, and vice-versa. If the primary purpose is to undertake other purposes, then neither the goods nor
   the service alternatives are available. The term business stop refers to either the goods purpose or the service
   purpose as appropriate. If the purpose selected for the subsequent stop is return, then the next stop location is
   already known, the final trip is made and the microsimulator moves on to the next tour. This return purpose is not
   available on the first trip. Figure 2 illustrates a typical next stop purpose decision.
             Consideration of next stop purpose, along with the other ‘iterative’ elements of the model (including next
   stop location and stop duration) is divided into 13 ‘segments’, where a segment combines an establishment industry,
   a vehicle type and a tour/stop purpose. (The next stop purpose model uses the tour purpose; the other two use stop
   purpose.) These 13 segments were chosen based on the preliminary estimation results and in order to ensure a
   suitable amount of estimation data for each segment.

               The generalized utility functions are as follows:
   Ubusiness =           business previous × ln(number of previous business stops)
                        + ASCbusiness
   Uother =              other previous × ln(number of previous other stops)
                        + other elapsed time × total elapsed time
                        + ASCother
   Ureturn =             total previous × ln(number of previous stops)
                        + elapsed time ×total elapsed time
                        + travel time × total travel time
                        + return gen cost × generalized cost for return to establishment
                        + ASCreturn


TRB 2005 Annual Meeting CD-ROM                                                             Paper revised from original submittal.
             Coefficients for the above are shown in table 5. The alternative specific constants indicate that other stops
   are universally not very common, except on other tours, and that tours tend to perform primarily business stops. The
   previous stop coefficients show that as a tour continues the propensity to make additional stops increase. However,
   the elapsed time and travel time coefficients will increase as additional stops are generated, pulling vehicles back to
   their establishments. In order to make a large number of stops, each stop must be made quickly – which is confirmed
   in the data, where security companies were found to visit 50 or more locations in a night, stopping for a minute or
   two at each.
             The total elapsed time includes the total travel time, so the positive values for the coefficient for total travel
   time indicates that the travel time component of the elapsed time has a further positive influence on the tendency to
   return to establishment. This suggests that travel time and the associated locations of clients being served in tours
   from establishments has more of an influence on the structure of tours than does the time spent at the clients. The
   influence of total elapsed time includes the influence of workshift patterns, with lunch break and quitting time
   occurring at fixed times some 4 and 9 hours of total elapsed time after the start of the workday. With hindsight, it is
   seen that additional variables indicating the differences in time to lunch break (perhaps noon) and quitting time
   (perhaps 4:00pm) would have been useful in helping provide a more complete representation of the influence of
   such factors along with the general tendency for tours to end more directly.
   Next Stop Location Model
            Once the purpose of the next stop is selected, assuming it is not return to establishment, then the location of
   the next stop is selected. Again, a Monte Carlo process is used with the selection probabilities – across the full set of
   1,447 model zones – determined using a logit model with a utility function.
              The generalized utility function for travel from zone i to zone j is as follows:

   Ulocation(j) =              land use
                           +     average income  × zone j average household income
                           +     gen cost of travel× generalized cost of travel from current zone to zone j
                           +     return gen cost × generalized cost of travel to return to establishment from zone j
                           +     population accesib × population accessibility
                           +     employment accesib × employment accessibility
                           +     enclosed angle × enclosed e-i-j angle
                           +     size term × ln (zone j population + relative employment size term × zone i employment)


                   is chosen based on the land use of zone j; based on the five land use types (low density, residential,
                land use
   commercial/retail, industrial and employment node), the value of land use is set to 0 for residential.

            enclosed e-i-j angle is the angle enclosed by the lines connecting the establishment and zone i, and zone i
   and zone j. This angle is measured in degrees, so a value of 180° represents travel directly away from the
   establishment, and a value of 0° represents travel in the direction of the establishment.

            The last part of this function is slightly different for transportation companies, where size term is not
   multiplied by zone population or by employment, but by the output of a separate visit regression model. This visit
   model simulates demand for transportation firms, and is based on the employment of a zone, the accessibility of the
   zone, the population of the zone, and the proportion of employment in various industries. It is included in this case
   in recognition of the significant role of transportation depots and the resulting appropriateness of increasing the
   accuracy of representation regarding their attraction of commercial vehicle trips.

            The coefficients estimated for the location choice model are shown in table 6, with the 0 value for
   residential land use omitted because of space restrictions. Tours are generally ceritas paribus most attracted to
   industrial areas, and least attracted to employment nodes. Industrial areas tend to house warehouses, production sites
   and other types of activity that need more goods provided.


TRB 2005 Annual Meeting CD-ROM                                                                        Paper revised from original submittal.
             The most significant value in the estimations is the generalised cost of travel; t-ratios were typically in the
   30-50 range. That commercial vehicles are cost-averse is no surprise. The role of return generalised cost was
   smaller, however it was significant, indicating that commercial vehicles do consider how far they are straying from
   base. The accessibility coefficients were generally less significant, and the negative signs are likely the result of

            A negative coefficient for enclosed angle indicates a tendency to circle back towards the establishment;
   however, there is a strong correlation with the return generalised cost, and it is worth noting that some of the
   positive values for this coefficient are in segments with high value of return generalised cost. The size terms have a
   strong impact as well, with different segments being attracted to employment and population in different ratios.

   Stop Duration Model
             The stop duration model is similar to the tour start time model. The duration of each stop being made by the
   vehicle is established and used to advance the clock keeping track of the start and end times. A Monte Carlo process
   is used to determine the duration of the stop, with sampling distributions based on observed durations. There are
   different duration distributions for each of the 13 ‘segments’; for instance, an other stop will tend to be shorter than
   a private service–service–light vehicle stop. The microsimulator then returns to the next stop purpose module and

   Model Calibration
             Following the estimation process, a series of calibrations were performed. The elements of the model are
   interdependent; for example, if the tour generation is adjusted, establishment locations are changed, which affects
   the decision to return to establishment and therefore tour lengths. This interdependence necessitates an iterative
   calibration process where several different elements are adjusted simultaneously.
            The model was calibrated to match five sets of targets:
   •   Tour generation by industry and geographic area (within the generation model);
   •   Tour length (adjusting ASCreturn in the next stop purpose model) ;
   •   Vehicle type and tour purpose proportions (adjusting a set of ASCtour purpose, vehicle for each company type);
   •   Total trip destinations for 13 super-zones, including intra-zonal proportions (adding a set of k-factors to the next
       stop location model); and
   •   Number of trips within the AM, PM and combined offpeak periods (adjusting the tour start time model).
            Fits were within 5% of observed values in all cases, and many are within a fraction of a percent; most of
   the coefficient changes are fairly small. Typical values for the added ASCtour purpose, vehicle are ±0.5, for instance.

   Model Capabilities
            The elements in the Calgary CVM permit a wide variety of responses to network changes. This enables not
   only the realistic simulation of commercial vehicle movements, but the testing of a number of possible scenarios.
   This model will respond to changes in areas such as:
   • Road network capacities and connectivity
   • Personal travel (congestion as a result of the personal travel model)
   • Truck routes
   • Tolls
   • Fuel taxes
   • Population and employment sizes and distributions (and associated growth and development)
   • Employment densities
   • Employment types (economic shifts between industries).

            The above changes will be responded to in most levels of the model. If travel becomes more onerous – if
   the network becomes more congested, or if a key truck route is removed – then vehicles will not just travel shorter
   distances. They will also make shorter tours, and more tours will be generated to fulfil demand. Tour generation,


TRB 2005 Annual Meeting CD-ROM                                                           Paper revised from original submittal.
   start time period, tour purpose, vehicle type, stop purpose and stop location are all influenced by network conditions,
   and all of them will respond to changes such as the ones listed above.

            The nature of urban commercial vehicle movement is not widely understood; the principles governing
   larger regional models are often incorrectly assumed to apply to urban areas. This discounts many important
   elements, including the importance of trip chaining, the significance of service delivery, and the role of light
   commercial vehicles. The development and use of models based on the freight-only, large-truck view of commercial
   movements in urban areas leads to the neglect of the half of commercial trips made in light vehicles and the half of
   trips made for service delivery. Urban commercial movement is different from both household movement and
   regional commercial movement, and thus requires different modelling.
            The urban commercial movements model described here is a successful demonstration of several
   principles. The generation model responds to network conditions in a realistic manner. The use of constants in the
   tour purpose and vehicle choice model still permits a broad spectrum of behaviours, and a reaction to changes in
   employment and population. A tour-growing model permits all stops on a tour to have importance, and permits tours
   of highly variable lengths. The interactions between each model component and the information from the network
   mean that changes to the modelled system affect the entire behaviour of commercial vehicles.

            Some areas of the model have potential for improvement; particularly the stop duration model.
   Additionally, the formulation of a more advanced fleet allocator model – perhaps based on a similar tour-based
   microsimulation or some form of use of the Clarke-Wright dispatch algorithm (11) rather than a gravity model – will
   complement the current tour-based microsimulation model. A more complete spatially disaggregated input-output
   land use and transport model would also provide a richer modelling environment to inform the decisions made by
   the CVM.

            Overall, the tour-based microsimulation approach has proven to be very successful: it delivers stable and
   robust answers, calculates quickly, gives great flexibility and permits very subtle influences and interactions to be
   modelled. The parameter estimations have provided great insight into the decision-making of urban commercial
   vehicles, and this model permits a good representation of this usually neglected component of urban traffic.


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       National Conference on Transportation Planning for Small and Medium-Sized Communities.


TRB 2005 Annual Meeting CD-ROM                                                         Paper revised from original submittal.
       9. Slavin HL (1979) The Transport of Goods and Urban Spatial Structure. PhD Dissertation. Cambridge
       University, UK.
       10. Hunt JD and DG Morgan (2001) The Calgary Region commodity flow survey. Proceedings of the 2001
       Annual Conference of the Canadian Institute of Transportation Engineers, Calgary AB, Canada, May 2001.
       11. Clarke G and Wright JW (1964) Scheduling of vehicles from a central depot to a number of delivery points.
       Operations Research 12:568-581.


TRB 2005 Annual Meeting CD-ROM                                                    Paper revised from original submittal.

   TABLE 1 Tour Purpose and Vehicle Choice Alternative Specific Constants
   TABLE 2 Tour Purpose and Vehicle Choice Land Use and Company Type Constants
   TABLE 3 Tour Purpose and Vehicle Choice Accessibility Coefficients
   TABLE 4 Tour Purpose and Vehicle Choice Transportation Company Coefficients
   TABLE 5 Next Stop Purpose Coefficients
   TABLE 6 Next Stop Location Coefficients

   FIGURE 1 Process flow of Calgary CVM.
   FIGURE 2 Hypothetical tour growing illustration.
   FIGURE 3 Proportions of tour purposes and vehicle choices for a wholesaling company establishment located in
            different land use zones.

   TABLE 1 Tour Purpose and Vehicle Choice Alternative Specific Constants
                        Light vehicle         Medium vehicle  Heavy vehicle
   Service tour         0                     -3.944          -4.466
   Goods tour           -3.053                -5.393          -5.700
   Other tour           -3.705                -7.481          -9.620
   All values significant at the 99.9% level (t-ratio > 3.29)

   TABLE 2 Tour Purpose and Vehicle Choice Land Use and Company Type Constants
                                      Light          Medium         Heavy         Service         Goods tour   Other tour
                                      vehicle        vehicle        vehicle       tour
   Low-density land use               0              2.047          2.461         0               .3460        1.603
   Residential land use               0              0              0             0               0            0
   Commercial / retail land use       0              5.023          3.145         0               -.9187       .6176
   Industrial land use                0              2.391          2.144         0               -.1517       1.012
   Employment node land use           0              2.861          1.652         0               .01522**     .7474
   Private service company            0              0              0             0               0            0
   Retail company                     0              -1.529         -2.386        0               3.105        -.04871**
   Industrial company                 0              .5347          .9954         0               2.018        .4485
   Wholesaling company                0              .4309          .3835         0               3.946        -.2193**
   * indicates significance at the 95% level (t-ratio > 1.96) but not at the 99.9% level (t-ratio < 3.29)
   ** indicates significance at less than the 95% level (t-ratio < 1.96)
   All other values significant at the 99.9% level (t-ratio > 3.29)


TRB 2005 Annual Meeting CD-ROM                                                         Paper revised from original submittal.
   TABLE 3 Accessibility Coefficients affecting Vehicle Choice in Tour Purpose and Vehicle Choice Model
                        Low-density          Residential land Commercial /            Industrial land     Employment
                        land use             use                  retail land use     use                 node land use
   Light vehicle        4.207×10-5           0                    4.860×10-5          2.262×10-5          3.512×10-5
   Light vehicle        0                    0                    1.027×10-4          0                   0
   Medium vehicle       0                    0                    0                   9.866×10-6          0
   Medium vehicle       0                    0                    9.705×10-5          0                   1.414×10-5
   Heavy vehicle        1.356×10-4           0                    4.891×10-5*         0                   1.736×10-6**
   Heavy vehicle        0                    0                    1.057×10-4          0                   0
   * indicates significance at the 95% level (t-ratio > 1.96) but not at the 99.9% level (t-ratio < 3.29)
   ** indicates significance at less than the 95% level (t-ratio < 1.96)
   All other values significant at the 99.9% level (t-ratio > 3.29)

   TABLE 4 Coefficients for Transportation Companies in Tour Purpose and Vehicle Choice Model
                                      Light vehicle       Medium vehicle Heavy vehicle
   Alternative-specific constant      0                   .7417               1.897
   Low-density land use               0                   .02558**            -.1049**
   Residential land use               0                   0                   0
   Commercial / retail land use       0                   0                   .6191*
   Industrial land use                0                   0.5390              -.2808*
   Employment node land use           0                   -.2192**            -2.163
   Employment accessibility           0.1268×10-4         0                   0
   * indicates significance at the 95% level (t-ratio > 1.96) but not at the 99.9% level (t-ratio < 3.29)
   ** indicates significance at less than the 95% level (t-ratio < 1.96)
   All other values significant at the 99.9% level (t-ratio > 3.29)


TRB 2005 Annual Meeting CD-ROM                                                         Paper revised from original submittal.
TABLE 5 Next Stop Purpose Coefficients
Company types / Tour ASC              ASC Other ASC              Business     Other        Total         Elapsed    Travel time Other        Return      Employ.
purpose / Vehicle        Business                  Return        previous     previous     previous      time       (×10-3)     elapsed      gen. cost   accessib.
type(s)                                                                                                                         time         (×10-2)
All / Other / All        n/a          0            4.083         n/a          0            -3.380        .7893      0           0            26.96       7.015×10-7**
Private service /        2.936        0            2.639         .3514*       .2715**      -1.045        .2539      5.969       .1046*       3.981       0
Service / Light
Private service /        2.352        0            2.162         .4774        1.053        -.7774        .3402      2.587       .1048        6.057       0
Service / Medium and
Private service /        2.284        0            1.648         1.133        1.336        -.5174        .3909      6.431       .2716        1.106** 0
Goods / All
Retail / Service / All 2.707          0            2.619         .6021        .9202        -.1112**      .1837      -.8995**    .1532        5.538       0
Retail / Goods / All     3.725        0            3.411         .1141**      1.557        -1.519        .2083      8.930       -.1128**     -3.348      0
Industrial / Service / 2.525          0            2.978         1.075        1.121        -.9242        .3525      3.123       .2234        3.253*      0
Industrial / Service / 2.599          0            2.364         6.148        1.202        -1.133        .3025      9.960       .1187        10.75       0
Medium and heavy                                                 ×10-2**
Industrial / Goods /     2.890        0            3.041         .3996        .9585        -1.127        .2748      4.555       .1103        3.335       0
Wholesale / Service / 2.302           0            2.028         .9692        1.159        -.3461*       .3419      2.754**     .1509        9.744       0
Wholesale / Goods / 3.448             0            1.823         .4821*       1.412        -.4929*       .2715      4.501*      -.1719       1.402** 0
Wholesale / Goods / 2.984             0            1.687         .3894        1.316        -.4665        .1746      10.28       6.591        2.118** 0
Medium and heavy                                                                                                                ×10-3**
Transportation / n/a / 2.901          0            2.541         1.395        2.174        6.366         .2944      1.819       .2447        7.048       0
All                                                                                        ×10-2**
* indicates significance at the 95% level (t-ratio > 1.96) but not at the 99.9% level (t-ratio < 3.29)
** indicates significance at less than the 95% level (t-ratio < 1.96)
All other values significant at the 99.9% level (t-ratio > 3.29)


                  TRB 2005 Annual Meeting CD-ROM                                                             Paper revised from original submittal.
TABLE 6 Next Stop Location Coefficients
Company types /       Low          Comm. / Industrial Employ. Average Gen. cost Return .                   Population   Employ.     Enclosed   Size term Relative
Tour purpose /        density      retail land land use node land income           of travel gen. cost     accessib.    accessib.   angle                employ.
Vehicle type(s)       land use use                         use          (×10-6)                            (×10-6)      (×10-6)     (×10-3)              size term
All / Other / All     -.7902       .02702** -.1595*        -.6126       -11.49     .3039         .1310     -7.651       -9.696      -2.346     .2800     6.779
Private service /     -.08976* -.2755          .2152       -.4623       1.676*     .3283         0         -10.83       -2.653      -1.884     .3094     .08658**
Service / Light
Private service /     .7250        -.1057** .4655          -.7546       9.476      .08481        .1229     -44.65       9.296       3.684      .2219      0
Service / Medium
and heavy
Private service /     -.3327*      .5674       .4926       .2062        0          .5688         0         5.717*       -16.54      -6.348     .1588      0
Goods / All
Retail / Service /    -.9676       -.2310      .1547*      -.5132       0          .3601         .03662    -17.35       0           -1.241*    .2841      .6334
Retail / Goods / All -.1707        -0.0256** .8014         -.1840       0          .3734         .09158    -13.32       -1.682*     1.914      .2067      1.633
Industrial / Service -1.144        -.2361      .05026** -.4182          0          .2869         0         -24.98       5.477       -3.067     .2371      1.231
/ Light
Industrial / Service 0             -.3231*     .2789*      -.8438       0          .1627         .1279     -13.25       -16.96      2.934      .1205      1.012**
/ Medium and
Industrial / Goods / 0             -.1497      .5575       -.2042       0          .2581         .09615    -10.68       -5.139      -2.146     .2722      0
Wholesale /           -.9340       -.2130*     .1440**     -.4410       2.367**    .3849         .04300*   -11.81       -27.71*     1.761      .2426      2.138
Service / All
Wholesale / Goods -.6668           0           .9271       -.2688       0          .4495         .1075     -32.84**     -67.74      .8923      .2248      0
/ Light
Wholesale / Goods -.1226** 0                   .1445*      -.1183*      0          .3123         .03430    -31.84       5.950       -1.431*    .3021      2.313
/ Medium and
Transportation / n/a -.5279        .1004       .6275       .02668** -4.691         .3792         0         -11.72       -5.984      3.109      8.651×10-2 n/a
/ All
* indicates significance at the 95% level (t-ratio > 1.96) but not at the 99.9% level (t-ratio < 3.29)
** indicates significance at less than the 95% level (t-ratio < 1.96)
All other values significant at the 99.9% level (t-ratio > 3.29)


                  TRB 2005 Annual Meeting CD-ROM                                                           Paper revised from original submittal.
                                                   Tour Generation

                                              Vehicle and Tour Purpose

                                                      Tour Start

                                                  Next Stop Purpose

                                                  Next Stop Location

                                                    Stop Duration

   FIGURE 1 Process flow of Calgary CVM.

                                         Client                             Non-client
                                                                        (e.g. Gas Station)


                              (e.g. Restaurant)


   FIGURE 2 Hypothetical tour growing illustration.


TRB 2005 Annual Meeting CD-ROM                                                   Paper revised from original submittal.
Stefan, McMillan and Hunt                                                                                                                                               16




                                                                                                                                                                  Heavy other
   Proportion of generated trips

                                                                                                                                                                  Heavy goods
                                                                                                                                                                  Heavy service
                                                                                                                                                                  Medium other
                                   50%                                                                                                                            Medium goods
                                                                                                                                                                  Medium service
                                                                                                                                                                  Light other
                                                                                                                                                                  Light goods
                                                                                                                                                                  Light service



                                           Low-density land use   Residential land use   Commercial / retail land   Industrial land use    Employment node land
                                                                                                 use                                              use

FIGURE 3 Proportions of tour purposes and vehicle choices for a wholesaling company establishment located in different land use zones.


                                          TRB 2005 Annual Meeting CD-ROM                                                 Paper revised from original submittal.