MULTIMODAL INVESTMENT ANALYSIS METHODOLOGY PHASE ONE: THE CONCEPTUAL MODEL

PART VI. PASSENGER TRANSPORTATION

Introduction

The section deals with various factors and aspects that should be taken into account in forecasting the demand for intercity passenger transport. It discusses intercity passenger travel behavior and how this affects the demand for different modes of transportation. Also, this section identifies the different trip purposes that generate intercity travel demand and the different characteristics of each trip purpose. It focuses on trip generation and distribution factors, and it formalizes the functional relationships between transportation service demand and the factors associated with each trip. These relationships provide the basis for developing models used to forecast intercity travel demands and the choice of modes of transportation.

 

Delineation of trip purposes

The most common trip purposes in intercity travel are journey to work, business and non-business. Non-business trips include trips made for shopping, recreation and personal business. The journey to work is made from a person’s place of residence to place of employment, which may be a manufacturing plant, a retail store or shopping mall, or a public or private institution, such as hospital or university. The journey to work includes both the trips from home to work as well as the return trip. In the context of intercity travel, the journey to work include trips that cross municipal boundaries (i.e., commuter trips) and does not include trips within an urbanized area. Business travel is related to the performance of the traveler’s work. Examples of business travel include: sales representatives’ everyday travel like product sales and service delivery, obtaining training, travel to business meetings and conferences, travel to company’s head offices and trips associated with government business.

Non-business travel includes personal travel not related to one’s work. Recreation travel may be related to a traveler’s vacation, although it could also be intended for other specific recreational activities such as attending sports events, concerts, etc. Recreation travel can be expected to exhibit elasticity characteristics that are reverse of those of business travel. The value of time and money is different for different types of trips. Business trips give priority to time, while recreation trips are dependent on the cost of the travel. Shopping trips are trips to any retail outlet, regardless of the size of the store and whether or not a purchase is actually made. These trips usually have more frequency than recreation travel, usually shorter distance than both recreation travel and personal business, and are made at different times of the week. The most common purpose under personal business travel is visiting friends and relatives, which is usually referred to as vfr. This could also include trips to doctors, lawyers or other special services not found locally.

Table 1. Relative ranking of different modes in terms of the attributes.

SYSTEM ATTRIBUTES

DIFFERENT MODES OF INTERCITY PASSENGER TRAVEL

Automobile

Air

Railways

Travel time

3

1

2

Cost

3

1

2

Schedule frequency

1

3

2

Reliability

1

2

3

Convenience

1

2

3

Flexibility

1

3

2

Accessibility/ Completeness of journey

1

3

2

 

Identification of Trip Characteristics

Trip characteristics influence mode choice. The most important characteristic that influence mode choice is the length of the journey and the trip purpose. Other important trip characteristics that affect mode choice includes frequency of the travel, number of people making the trip, seasonal variation, and the amount of baggage carried on the trip.

 

Trip length

The demands for intercity travel over different trip lengths exhibit fundamentally different characteristics. They have different elasticities to their various determinants and follow different temporal patterns. Consequently, the stratification of trip length is an essential disaggregation in intercity demand analysis. In intercity transportation analysis two types of travel are distinguished, long haul and short-haul. A distance criterion of one thousand kilometers is used to distinguish between long haul and short haul trips. But this is not a fixed threshold and depends on the characteristics of the transportation system available.

Long-haul travel is usually less elastic with respect to the attributes of the transportation system than is short-haul travel. One reason for this is the limited extent of choice in long-haul travel. This is because, as the trip length increases, the number of alternatives (modes, routes, etc.) with comparable levels of service decreases, the extent of which depends on the availability of transportation systems and the geographic nature of the region. Long-haul travel is not very sensitive to transportation attributes such as schedule frequency and modal access and egress times because the travel time is usually so large that the variations brought about by changes in these attributes are not very determining. When dealing with long-haul traffic in the agggregate sense, it is usually sufficient to treat business travel on an annual basis and non-business travel on a seasonal basis.

Short-haul travel often involves a priority choice of mode and a choice of route available to the traveler. In addition, the travel time is usually sufficiently small so those attributes such as frequency and access characteristics become important determinants. The analysis of short-haul travel demand is almost like that for urban travel. Short-haul trips are made with greater frequency than are long-haul trips and exhibit weekly, and sometimes even daily, temporal patterns. In this case analysis on an annual basis will not be sufficient. In many intercity corridors where commuting work trips are made or where multi-modal transportation networks are so ubiquitous that a mode choice situation arises that is similar to urban travel and urban models are used (1). Table 1 displays mode choice percentages for mid-west business travelers, showing also the ranges of distance traveled (by respondents to the Bureau of Transportation Statistics survey). Automobile was selected by 76 percent of travelers, and highway travel, in general, is clearly dominant when rentals and truck transportation (and bus, included in "other") are added.

Table 1. Distance traveled and mode choice for business travel in the Mid-West

Distance (Miles) Mode Percent of Total

68-1251

Car

76%

15-4722

Truck/Rental

12%

12-2750

Commercial Plane

9.5%

91-1994

Rail

0.2%

76-2146

Other

2.3%

Source: 1995 American Travel Survey, Bureau of Transportation Statistics

 

Trip Purpose Characteristics

Trips may be compared by their elasticities of income of the traveler, cost of the trip, travel time, and by their seasonality. Journey to work trips can be expected to be income elastic, cost inelastic, and travel time elastic. They are temporally uniform throughout the year. Business travel (short haul) trips can be expected to be cost inelastic because the trip cost is usually not a personal expense but rather paid out of a corporate budget of which it is a negligible proportion. These trips are also travel-time elastic and elastic with respect to other convenience-related attributes such as schedule frequency and reliability of service. Business travel can be expected to have temporal pattern that is fairly uniform year round, except during major holidays and in situations where institutionalized vacations cause a drastic decrease in business activities during a specific period of the year. Long haul business travel can be expected to be not very sensitive to transportation attributes such as schedule frequency and modal access and egress times because the travel time is usually so large that the variations brought about by changes in these attributes are not very determining. This is true when dealing with business travel in the aggregate and not true when dealing with individual business travelers.

Recreation travel can be expected to be highly cost elastic, but lower time and convenience elastic than journey to work and business trips. The reason for this is that the cost of a recreation trip is usually a personal expense. Recreation trips usually exhibit strongly peaked temporal patterns with peaks during vacation seasons and special recreational events. Vacation trips are more cost inelastic than are recreation trips because transportation costs generally account for only a portion of the total vacation costs.

- Personal business travel has characteristics similar to recreational travel. However, these two types of non-business travel have a distinguishing factor with regard to their elasticities. The transportation cost is normally only a proportion of the total cost of the vacation and recreational trips but is almost the total cost of a vfr personal business trip. Vfr travel can be expected to have higher transportation cost elasticities than recreation and vacation travel. On the other hand, where a choice of destination is available, a vacation traveler can conceivably change destinations if the transportation cost to one increases, flexibility the vfr traveler does not have. This would have the opposite effect on elasticities. The extent to which these two effects cancel out depends on the availability and practicality of destination choice. This also determines whether the combination of the two trip purposes, i.e., vfr travel and vacation or recreation travel, can be made to one common practical destination (1).

 

Other Trip Characteristics

Trip frequency also influences mode choice, as does trip length. A frequently visited place, like the business headquarters might be made by airplane, if it is very far and if the trip purpose is time sensitive. Again, if the destination is not so far, like a shopping plaza, an automobile will make the trip.

The size of the group traveling and income of the travelers also influence mode choice. Operating costs of a car are fixed, in terms of the number of passengers. On the other hand, the costs of traveling by a common carrier are directly related to the number of passengers. For long journeys, which require overnight stays and meals, mode choice is considerably influenced by group size. Seasons, too, influence mode choice decision. In the winter, the not-so-far places are usually traveled by a common carrier, while an automobile would reach the same destination during the summer months.

The weight and the quantity of baggage also affect mode choice. For example, a passenger from San Jose, California who has a new job in Ames, Iowa, and plans to bring all his furniture from San Jose, will consider two modes for his travel. First, he will transport his furniture through a mover, and he and other family members will come to Ames by either automobile or airplane. The amount of baggage made necessary the model choice of truck transportation in addition to the personal car or commercial airplane. (Other modes, such as railroad or bus, are possibilities but probably less practical for the origin and destination in this example.)

The above example may be influenced by the need for a vehicle at the destination. Assuming the family owned an automobile before the move, the need to move it to the new location may be the greatest influence in the modal choice for one or more family members. However, if they were uncertain of the car’s ability to withstand the rigors of a long trip, the alternative of disposing of the car in California and replacing it at the new location, after arriving by air, becomes more likely. Business travel is often assumed to be more time-sensitive than cost-sensitive, resulting in longer distances being covered by air travel, with vehicles being rented at the destinations; the alternative would be to drive the entire trip, ensuring the availability of a vehicle at all times and avoiding higher variable costs of short-term rentals. On the other hand, if the trip purpose is recreation, even a very long trip might be taken entirely by an automobile.

Decisions about mode choice are made by passengers, taking into account these and other interrelated trip characteristics. Table 2 shows how intercity passengers rank transportation system factors of travel time, cost, frequency of schedule, reliability, convenience, flexibility, accessibility and completeness of journey. Automobiles, although ranked last in terms of travel-time and cost, were first for all the other variables. Competing modes, such as air, may benefit from addressing their relative weakness in scheduling, flexibility and accessibility.

Table 2. Relative ranking of different modes in terms of their attributes.

SYSTEM ATTRIBUTES

DIFFERENT MODES OF INTERCITY PASSENGER TRAVEL

Automobile

Air

Railways

Travel time

3

1

2

Cost

3

1

2

Schedule frequency

1

3

2

Reliability

1

2

3

Convenience

1

2

3

Flexibility

1

3

2

Accessibility/ Completeness

of journey

1

 

3

2

 

Intercity Passenger Service Demand Model

Trip generation factors

In passenger trip generation modeling there are usually two parts, personal trip productions and attractions. Personal trip production factors include income, car ownership, household structure, family size, land value, population density and accessibility. Consideration of income, car-ownership, household structure and family size have been usually used in trip generation studies (disaggregate approach), while the value of land and residential density are typical of zonal studies (aggregate approach). Accessibility has also been used in some studies because it offers a way to make trip generation elastic (responsive) to changes in the transport system. Personal trip attraction factors include roofed space available for industrial, commercial and other services, like certain professional services such as doctors and lawyers. Another factor used has been zonal employment and some studies have attempted to use an accessibility measure (2).

 

Approaches to Intercity Demand Analysis

Three approaches have evolved for the analysis of intercity transport demand: the multimodal approach, the abstract mode model, and the mode-specific approach (1, pp. 223-230).

Multimodal Approach

The multimodal approach recognizes that the demands for travel by different modes are related and should be analyzed simultaneously. Short-haul travel, including urban corridor travel, is done by the multimodal approach. It takes into account the fact that demand for short-haul intercity travel, demand for different modes are related, and so there is need to consider the cross-elasticities of the demand by mode type. The multimodal models of intercity travel postulate that the demand for travel between a pair of cities by a particular mode is a function of the socioeconomic characteristics of the cities and of the supply attributes of that mode, plus those of all other modes available. As such, the multimodal model is a combined destination and mode choice model with an implicit assumption that these choices are made simultaneously. These types of models often suffer from collinearity problems.

 

The Abstract Mode Model

The abstract mode model simplifies the general multimodal transport model by specifying a demand function where for each of the supply variables, the values of only two modes are present: the mode in question and the mode offering the best value of the attribute in question. The data needs would include city population, per capita income, percent of total employment that is in manufacturing, travel time by mode m relative to best travel time; best travel time between the cities, travel cost by mode m relative to best cost; and best travel cost between the cities. This approach does not account for the social and economic character of city- the intrinsic characteristics of the city. The theory of intrinsic characteristics postulates that the effects of the conventional demand variables on intercity travel vary with economic and social characteristics of the city and these cannot be represented adequately by quantitative variables. This observation was first made by Lansing and Suits, who calibrated two simple gravity models, one for New York and the other for Chicago (1, pp. 227-228). These models related total traffic between each of the two cities and a selected number of cities. The variables considered were population, per capita income and distance. The differences between most of the parameter values of the two models were significant. Lansing and Suits explained these differences from the qualitative knowledge of the difference of the two cities. The major difference was noticed in the distance elasticity, showing travel to decline faster with distance from Chicago than New York. This may be partly due to the geographic locations of the two cities and also the fact that Chicago is more of a regional market center whereas New York is more of a national and international center. In order to incorporate this theory of intrinsic characteristics in the conventional regression model, the error term was decomposed into two additive components: one for intrinsic characteristics and the other for random errors. They developed a generalized least-squares estimation procedure, assuming the intrinsic characteristics to be same for all modes but different for each city pair. The results showed that the generalized least-squares method produced parameter estimates that were much superior to those produced by the ordinary least-squares method. This model modification did help in improving the statistical fit, significantly but it also had drawbacks. The model can no longer be used as it is for any city pairs other than those for which it was calibrated, and the second is that even for those city pairs, the model can be used for forecasting only if it is assumed that the "intrinsic characteristics" of the cities remain constant over the forecasting period. It also cannot forecast travel on novel modes or technologies not available at the time of calibration (3).

Mode-specific Approach

This approach is based on the proposition that the demands for travel by different modes are independent, or can be assumed so, and therefore can be analyzed separately. In general, long-haul travel is handled with a mode-specific approach. The mode-specific analysis for long-haul travel is valid when the characteristics of the transportation system are such that no significant competition is likely to exist between modes. The most important developments in mode-specific approach are those in the demand for air travel. This is not only because it is the most important mode of intercity travel but also the assumption of independence is best justified for this mode. This is true particularly in long-haul travel and in terms of travel time, where air is far superior to other modes, catering to a segment of the population for whom travel time is the most important supply attribute, and for whom other attributes such as cost (the air mode does not have an advantage here) are relatively less important.

On the other hand, travel by long haul intercity bus is much cheaper and it caters to a segment of the population whose most important supply attribute is cost; time can be sacrificed for this economy. It has been customary to analyze their travel demands separately, but only in the short run. Mode-specific models cannot be used to analyze long-range times series travel data or for long-range forecasting. It is evident from the study of the histories of modal evolution that even in long-haul travel, shifts have occurred between modes (e.g., from rail transport, which peaked in the 1940s, to air) and there are long-run cross-elasticities. This approach can explain observed trip behavior better and allows for qualitative idiosyncrasies concerning mode choice that are otherwise neglected in model variables. But the assumption of that there is no competition between the various modes is limiting. It also cannot forecast travel on novel modes or technologies not available at the time of calibration.

The multimodal approach appears to be a realistic approach. In the real world, decisions regarding destination and mode choice are done simultaneously, taking into account the other modes of travel available. This approach also takes into account the socio-economic variables of the city pairs concerned. Another aspect that need to be included in this approach for it to be the ideal one for demand forecasting is consideration of the theory of intrinsic characteristics of cities. The data needs for this approach will be disaggregated data and sometimes might not be very easy to find and might involve complex iterations. But in this age of information technology and its high pace of advancement, that should not be a major problem.

 

Sequential models

: Sequential models consist of aggregate and deterministic demand type models, behavioral models with a disaggregated approach, entropy models of aggregate approach and simulation models. Aggregate and deterministic demand type models calculate the trip distribution on regions normally by a gravity or intervening opportunity method. The trip production function was assumed not to be dependent on travel time, price or frequency. The unit of observation for an aggregate approach is generally a traffic zone. The gravity model states that the number of trips made between an origin and a destination is positively related to the number of trips leaving the origin and to the pull attributes of a destination but is inversely related to the distance between the origin and the destination. Two basic differences exist between an intervening opportunities model and a gravity model. In the intervening opportunities model the in situ characteristics of a destination are measured simply as the number of opportunities available there. The second difference is in how the effect of geographical separation between places on the number of trips made between them is conceptualized. In the gravity model, distance per se is supposed to influence the chance of a trip being made, whereas in the intervening opportunities model, the critical factor is the number of other opportunities closer to an origin than any particular destination being considered by a traveler (4). The trip mode distribution is normally a function of simply the differences in travel time or generalized cost. The assignment of flows to the network generally adopts an all or nothing approach with or without capacity constraints (5).

The unit of observation for a disaggregate model is the individual or household. In general, disaggregate models or discrete choice models postulate that the probability of individuals choosing a given option is a function of their socioeconomic characteristics and the relative attractiveness of the option. To represent the attractiveness of the alternatives the concept of utility is used. In order to predict if an alternative will be chosen, the value of its utility must be contrasted with those alternative options and transformed into a probability value between 0 and 1. For this a variety of mathematical transformations exist which are typically characterized as discriminant, logit and probit functions. This type of model has a disaggregated data need that is not easily satisfied. It is also based on complex calculations.

Entropy models of aggregate behavior give the same result as the gravity model. The gravity and intervening opportunities models can only be applied in situations where data have been collected on the observed distribution of trips at some point in time and on attributes of the destinations. When these data are not available, the so-called "entropy maximizing" approach is used. A system is made up of a large number of distinct elements. A full description of this system would require complete specification of its microstates. This would mean identifying each individual traveler, its origin, destination, mode, time of journey, etc. For practical purposes, a more aggregate or meso-specification might be sufficient. For example, a meso state may just specify the number of trips between each origin and each destination. In general, there will be numerous and different micro states which produce the same meso state. In a higher level of aggregation, a macro state, the data will specify the total number of trips on particular links, or the total trips generated and attracted to each zone. Estimates about the future are usually restricted to macro-state descriptions because of the uncertainties involved in forecasting at more disaggregate levels. It is easier to forecast the population of a zone than the number of households in a particular category residing in each zone. The basis of this method is to assume (unless there is information to the contrary), all microstates are consistent with the information about macro states are equally likely to occur. This approach enforces consistency about knowledge about macro states by expressing the information as equality constraints in a mathematical program. To find out about the meso state descriptions of the system, this system would identify those meso states which are most likely, given the constraints about the macro states. The aim of the approach is to provide the least-biased estimate of some missing data, such as trip distribution patterns, given just partial information on these data (2, p. 162).

Simulation models consist of two connected sub-models: an economic model based on input-output tables provides the transport model with data; and the second model assigns commodity flows to the transport network. The model involves a large number of comparisons by which the consistency of different economic and physical data may be evaluated. These in turn make possible to identify obvious weaknesses in the data and to sort the more reliable sources. The model also provides a systematic procedure for generating estimates by extrapolations or interpolations to fill gaps in the data. This macroeconomic transport simulation model (METS) encompasses many functional relationships and attempts to identify correspondingly a large number of behavioral regularities. This METS model is thus less dependent on simple extrapolation of historical trends. It can produce as by-product estimates of certain data series that might not otherwise be available and that are not estimated very easily or inexpensively. The model is a source of data estimates and also a consumer of them. Another important aspect of this model is that it contains a number of sub-models of the transport system, which can be operated independently of the larger model and at relatively low cost. This model was applied to evaluate alternative strategies for developing the Colombian economy and transportation system (6).

 

Transport System Models

Transport System Models are applications of economic theory to transport situations. One finds equilibrium in the transport market by setting up a supply function and a demand function and solving for equilibrium flows. Supply function factors include travel time, travel cost, travel frequency, safety and comfort. Demand function factors include population characteristics, employment characteristics, income, urbanization factor, distance, purpose and ownership of cars. The equilibrium occurs in a network affected by capacity constraints, topology and structure of the transportation system. The four-step approach known from urban transport models has been changed into a single step, and was developed for forecasting intercity passenger transport in the North-east Corridor of the United States by Kraft, McLynn, Quandt and Baumol. In DODOTRANS system (Decision Oriented Data Organized Transport Analysis System, developed by Manheim and Ruiters) of computer models from MIT and ICES (Integrated Civil engineering Systems), these models are available as an application of systems analysis to intercity problems (5, p. 179-180).

 

The Basic Structure and Approach of the Passenger Service Demand Model

The conceptual model for intercity passenger demand has been developed on the basis of literature review on transport modeling. This model incorporates the strengths of the existing transport models sometimes in its original form, sometimes as modified models. The conceptual model is an integration of the various models (and sub-models) encompassing the strengths of the existing models.

The conceptual model is based on the framework developed by Manheim for intercity transport (5, p. 180). The three steps of the conceptual model are: (a) estimation of passenger traffic volume between cities, (b) distribution of traffic between modes, and (c) distribution of the transport volume for mode m, between n routes of that mode between the two regions. The model has been schematically represented in Table 3, which illustrates a general conceptual methodology of intercity passenger transport demand forecasting. Transportation is assumed to be derived demand and so the variables that are considered exogenous are income, employment and population in this passenger model.

 

Estimation of Passenger Traffic Volume between Cities

Estimation of the total transport volume between two regions depends on the size of population, commercial character of the cities and the distance between them. This is usually done with the help of gravity models, intervening opportunity models and entropy models. Three elements that need to be taken into account when modeling the demand for transport between cities are the number of trips generated by a place/region; the degree to which the in situ attributes of a particular destination attract trip makers; and the inhibiting effect of distance.

Trips generated by a place/region can be calculated by linear regression analysis or category analysis based on attributes of that place/region, as used in urban transportation. Variables used usually try to portray the different features of the place/region in question. First, the potential number of trip makers in a zone should be identified. This is done by considering the size of population and the land use pattern of the origin. Second, the degree to which a potential trip maker’s characteristics affect his or her propensity actually to make a trip should be considered. This mainly depends on income, family size, car-ownership, etc. The trip purpose also plays a significant role in determining the demand for transport. Lastly, the geographic accessibility of the zone to potential destinations can also affect the number of trips made.

The in situ (the theory of intrinsic characteristics) attributes of a particular place refers to the characteristics of a place to pull travelers to the opportunities offered there. The attractiveness of a place/region will vary depending on the type of trip. For example, journey to work travel will depend on the destination’s employment opportunities while shopping trips and recreation travel will depend on the number of shops and entertainment opportunities, respectively.

The third element refers to how the remoteness of a destination may inhibit its ability to attract travelers, even when its in situ attributes might be favorable. In geographic terms, the second element refers to the site characteristics of a destination, whereas the third element refers to attributes of its situation. If a trip is more easily carried out because nearby places can fulfill its purpose, then that activity is relatively cheap, and an individual would be inclined to perform that activity relatively more often, other factors being equal (4, pp. 105-111). The factors influencing trip generation are tabulated in Table3.

The end result of this step comes up with the total demand for transport between two regions/places, disaggregated by trip purpose. It estimates the number of person trips per unit time from origin i to destination j. The functional relationship of this step can be represented as follows:

t

Xij = (EiEj)/ Dij

where, Xij = Total demand from origin i to destination j, at time t

Ei= Total trip generated from origin i

Ei= Ai f { a i, b i, c ( r), d (l) }

where, Ai = Intrinsic qualitative factor of zone i

a i = vector of explanatory variables for trip production by trip purpose from zone i

b i = vector of socio-economic explanatory variables for trip production from zone i

c (r) = vector of infrastructural explanatory variables for transportation route r (Access)

d (l) = vector of infrastructural explanatory variables for network link l (Access)

Ej = Aj F ( aj, bj)

where, Aj = Intrinsic qualitative factor of zone j

aj = vector of explanatory variables (economic) for trip attraction, to zone j

bj = vector of explanatory variables for intervening opportunities within zone i and j.

Dij = Distance decay factor (distance between i and j)

 

Distribution of Traffic between Modes

Distribution of the total transport volume between different modes of travel, which depends on travel time, fare and service frequency. The choice of intercity travel mode, for both short haul and long haul travel depend on certain characteristics of the mode(s) in question and also traveler, and trip purpose characteristics. The distribution of trips with a given mode is proportional to the service frequency and inversely proportional to the fare and travel time.

Trip characteristics The length of the trip and its purpose influence mode choice more than any other trip characteristic. The length of a trip usually determines the viability of a mode and also the viability of other modes, taking into account the trip purpose and its characteristics. Business travelers generally place a greater premium on travel timesavings and have less schedule flexibility than non-business travelers. A related characteristic concerns the traveler’s need for transportation at the trip destination. If an automobile is required then using a private automobile for the intercity trip becomes relatively more attractive. The distance between the origin and destination also influence the mode choice. Length of stay will also affect the desirability of using a private automobile because rental car costs are directly related.

Service characteristics The most important service characteristics between traveler’s origin and final destination are travel time and travel cost. Thus the traveler is affected by the time and cost of the primary mode and also by the time and cost of any access trips or intermediate steps in the journey, which includes multiple journey links in the primary mode component, that contribute to the door-to-door trip time and cost. Additional service characteristics that will influence the choice of mode include convenience of departure times, reliability of the scheduled travel time, comfort, parking, other amenities and safety (as perceived by the traveler).

Traveler characteristics The two most important traveler characteristics affecting intercity mode choice are income and group size. Income influences the value the travelers place on their time and their willingness to pay for reductions in travel time or better passenger amenities. The size of the traveling group changes the relative cost of traveling by automobile versus by common carrier. Automobile operating costs are insensitive to the number of occupants, whereas common carrier costs increase with group size. Trips long enough to require a stop for meals or overnight accommodations will be substantially affected by party size (7). Table 4 outlines the factors influencing mode choice for each trip purpose. Thus the total demand for mode k, for time period t, between zones i and j can be functionally represented as follows:

t

Xijk = C ( Ei, Ej, Dij, Mijk)

where, Mijk = l ( Mtk, Msk, Mtrk)

where Mtk = vector of explanatory variables for trip characteristics

Msk= vector of explanatory variables for service characteristics

Mtrk = vector of explanatory variables for socio-economic characteristics

Bjorkman used a simple formula to forecast intercity traffic. He maintains that the rate of increase in traffic is proportional to the rate of increase in population, the rate of increase in purchasing power, and the rate of improvement of traffic service (5, p. 178). According to Bjorkman, the factors that influence traffic are change in population, change in urban/rural population, change in income, improvement in a particular mode, improvement in other modes, change in fares/cost of a mode and change in fares/cost of other modes. The traffic to and from an area increases in proportion to the product of the said factors and that some of the factors may be given more weight than others by means of power indices. Therefore, the total travel demand, for time period t, from origin i to destination j can be represented as S S S Xijk, which is the aggregation of total demand between i and j by all modes.

Distribution of the transport volume for a mode, between n routes of that mode between the two regions

The problem of forecasting flow equilibrium in transport networks is solved by supply and demand functions. The supply function for a link indicates cost increases as flow increases up to capacity. The demand function for a pair of regions relates how volume decreases as the cost increases. Furthermore, there exist flow distribution rules: individuals choose minimum cost route; public services choose routes that maximize their total consumers’ surplus (Wardrop's first principle), minimum journey time (5, pp. 178-179).

Solutions to the problem can be divided into three categories: (a) traffic assignment; (b) mathematical programming; (c) algorithms with fixed and variable demands. Traffic assignment does not prove equilibrium convergence; mathematical programming (linear and dynamic) cannot handle the large-scale problems; algorithms with fixed demand are not realistic- those without fixed demand were worked out by Manheim and by Gilbert in 1968 (the latter proved the equilibrium convergence). The trips between every pair of zones are assigned to the fastest route and traffic is added up on each link. The travel times of the links are adjusted according to the following formula:

ti= e[qi/C - 1] t0

where ti = travel time after i’th step of iteration

e = 2.718 ( basis of Napier’s logarithm)

qi = the flow on the link after the i’th iteration

C= the practical capacity of the link

t0= initial travel time corresponding to flow equal to C

Using the adjusted travel times a new set of fastest routes between all pairs of zones is found. The process is repeated until the changes in the link flows and in the travel times are sufficiently small from one step of iteration to the next one.

The conceptual model highlights the fact that the demographic and economic characteristics of a place/region are the main factors that explains the generation and attraction of passenger trips. Along with this the distance decay factor plays an important role. All these together make up the basis for determining the total travel demand in a place/region, disaggregated by trip purpose. Then this total travel demand (disaggregated by trip purpose) is distributed among the modes available to determine the travel demand by mode. The service characteristics, traveler characteristics and trip characteristics influence the mode choice decision. Thus, the final output is the total passenger demand, disaggregated by trip purpose and each of these demands is then disaggregated by the different modes available.

 

References

  1. Kanafani, Adib K. Transportation Demand Analysis. McGraw-Hill Book Company, 1983, pp. 221.
  2. Ortuzer, J. de D., and Willumsen, L. G., Modelling Transport, 2nd ed., John Wiley and Son, 1994, pp. 116-117, 147.
  3. Quandt, R., and Young, K. H. Cross-sectional Travel Demand Models: Estimates and Tests. Journal of Regional Science, 9, 1969, pp.201-214.
  4. Hanson, Susan (ed.). The Geography of Urban Transportation, 2nd ed. Guildford Press, New York, 1995.
  5. Rallis, Tom. Intercity Transport, MacMillan Press Ltd., London, 1977.
  6. Kresge, David T., and Roberts, Paul O. The Systems Approach to Transport Planning, in John R Meyer, ed. Techniques of Transport Planning. Brookings Institution, Washington, D.C, 1971.
  7. Transportation Research Board. In Pursuit of Speed: New Options for Intercity Passenger Transport, Special Report 233, National Research Council, Washington, D.C., 1991, pp. 101-102.

Multimodal Investment Analysis: Phase 1 Contents

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CTRE Communications: Marcia Brink
CTRE Webmaster: Michele Regenold

Website: www.ctre.iastate.edu/

Iowa State University--Becoming the Best