Travel Demand Modelling

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Application Area 2: Travel demand modelling: Travel behaviour research

Application Area 2: Travel demand modelling: Travel behaviour research

Uncertainty and variability of the supply and demand sides of the transport system, travel choice models mainly measure the uncertainty of the system, but not always attempt to capture the uncertainty in the mind of the traveler, and its effect on travel choices. In order to make practical use of travel-choice models in stochastic networks a link is required between objectively measurable uncertainty of the transport system and travelers' perception of that uncertainty. We can identify three key reasons why fuzzy sets theory might be relevant to applications in travel behavior. First, imprecision and vagueness are inherent to the traveler’s cognitive model of behavior and choice. Second, in the transport environment, the information obtained by the traveler in the formulation of preferences, decision variables, constraints and parameters is vague or not precisely measurable. Third, imprecision and vagueness as a result of perception errors, cognitive biases and subjective opinion may further dampen the quality and quantity of available information. Hence, fuzzy sets can be used to bridge modeling gaps of normative and descriptive decision models in travel behavior research(and bring us a step closer to the "strong" paradigm of AI).
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Exploring Travel and Activity Behavior in Transit-Oriented Developments: Insights Into Transportation Benefits and Travel Demand Modelling

Exploring Travel and Activity Behavior in Transit-Oriented Developments: Insights Into Transportation Benefits and Travel Demand Modelling

The accuracy o f measurement for key travel variables is a major concern, as household travel survey data generally are collected through self-reporting by survey respondents. A large body o f literature has found that trips reported or recorded in a household travel survey tend to be underestimated (Clarke, Dix, and Jones 1981; Son et al. 2012). Compared with the number o f vehicular trips detected by Global Positioning System (GPS) devices, usually 10-35% o f total vehicular trips were not reported in conventional travel diaries (W olf 2004). The groups who tend to underreport are young, male, low income, less educated, and unemployed individuals. Also, individuals making many trips and traveling long distances were associated with higher likelihood o f trip underreporting. In addition, certain trips were less likely reported in a trip diary, such as trips o f short duration and a discretionary nature. Trips made at the end o f the day tended to be underreported (Bricka and Bhat 2006; Stopher, FitzGerald, and Xu 2007; Son et al. 2012). Trip underreporting occurs for several reasons, including incomplete recall, memory decay, insufficient understanding, unwillingness to report, and carelessness. Also, response burden due to poor survey instrument design, lengthy questionnaire, and/or insufficient instruction can cause trip underreporting in household travel surveys (Clarke, Dix, and Jones 1981; Son et al. 2012). These findings indicate that measurement errors can prevalently occur in conventional travel surveys; therefore, importantly, the errors need to be checked.
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TRAVEL DEMAND MODEL DEVELOPMENT AND APPLICATION GUIDELINES

TRAVEL DEMAND MODEL DEVELOPMENT AND APPLICATION GUIDELINES

The assignment of automobile trips in travel demand modelling has traditionally relied upon the use of a single, static trip table representing daily vehicle trips, or perhaps a factoring of daily trips representing a peak period or peak hour. This vehicle trip table is then assigned to the roadway network using a variety of techniques, most commonly, Equilibrium Capacity Restraint. With the growth of HOV (High Occupancy Vehicle) lanes and Toll facilities this methodology has been extended through the creation of separate LOV (Low Occupancy Vehicle), HOV, and/or Toll vehicle trip tables (still static demand for a specific time period) that were assigned iteratively (using equilibrium capacity restraint) onto the network. Due to the multiplicity of policies, HOV is sometimes split into HOV2 (two occupants) and HOV3+ (three or more occupants). This iterative assignment of modes allowed for the expression of network attributes unique to a single mode, e.g. less congestion for HOV vehicles on HOV links that are forbidden to LOV vehicles, while preserving the interaction of the separate modes on mixed-flow facilities. This technique greatly expanded the range of facilities and policies that could be explored, but suffered from a poor understanding of the importance of the order of assignment of modes, and of how to measure the degree of equilibrium. Recent advances in assignment methods have lead to simultaneous multi-class assignment. Using simultaneous assignment, each iteration assigns all of the various modes while still allowing for the expression of mode specific attributes on individual links.
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Modelling the Demand for Money in Pakistan

Modelling the Demand for Money in Pakistan

R 2 = 0.54 F (6, 32) = 6.35 Auto χ 2 (1) = 0.69 Norm χ 2 (2) = 0.16 Hetro χ 2 (1) = 0.009 The results show that the dummy variables that are significant in the marginal equations of income (Equation 14) and inflation (Equation 12) are individually and jointly insignificant when included in the preferred model of real money demand. The joint significance F-statistics is F(2, 32) being 0.69. It is not surprising that the intervention variables are not significant in the preferred model because their influence is captured in the inflation and growth variables, as demonstrated by the marginal process (i.e., Equations 12 and 14). 5 This exercise also proves that the estimated parameters of the conditional mean equation remained stable against the identified external shocks. These identified shocks have caused instability in the parameters of marginal equations. Thus it can safely be said that the estimated model of money demand can be used for policy simulation.
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Tourism Demand Modelling and Forecasting

Tourism Demand Modelling and Forecasting

will contribute to the effectiveness of both business planning in the private sector and macroeconomic policy making in the public sector. Despite the practical importance, there has been limited literature focusing on this issue. Coshall (2000) employed spectral analysis to detect cycles within and between the time series of tourism flows by air and sea from the UK to France, Belgium and the Netherlands. The univariate spectral analysis found no business cycle-type oscillations except the seasonal cycles. However, the cross- spectral analysis identified the cycles of dependence of passenger flows on the exchange rate changes. Gouveia and Rodrigues (2005) used a non-parametric method to identify the tourism growth cycles using the data on monthly tourist nights spent in hotel accommodation in the Algarve from the main source markets. It concluded that there is a time lag between tourism demand cycles and economic cycles. Rosselló (2001) used the leading indicator approach to forecast the turning points of international visitor arrivals to the Balearic Islands from the UK and Germany. The empirical results suggested that the leading indicator approach is favourable in turning point forecasting. Two studies further examined the forecast accuracy in terms of directional change accuracy. Witt et al (2003) suggested that the TVP model is preferable to 4 other econometric models and two time series models in the short-run forecasting of directional change, but there is no clear-cut evidence when longer horizons are concerned. Petropoulos et al (2005) showed that the model which incorporates technical analysis techniques outperforms classic time series models in directional change forecasting competition. Future forecast accuracy evaluation studies should not only focus on forecast error magnitude, but also on turning points and directional change errors.
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The centralisation of inventory and the modelling of demand

The centralisation of inventory and the modelling of demand

APPENDIX 6.1 General Condition for Service Benefits I Disbenefits Theorem If demand is Poisson distributed with parameter J1 at each decentralised depot and the conditions for StuIman's [r]

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Utilizing advanced modelling approaches for forecasting air travel demand: a case study of Australia’s domestic low cost carriers

Utilizing advanced modelling approaches for forecasting air travel demand: a case study of Australia’s domestic low cost carriers

Another significant change which occurred in the third phase of Australia’s domestic airline market has been the evolution of the two incumbent airlines: Jetstar and Virgin Australia (previously called Virgin Blue) (Koo 2009). In recent years, Virgin Australia has increasingly focused on becoming a FSNC similar to Qantas. Virgin Australia’s business model is focussing on expansion into smaller regional markets with lower levels of demand (markets being served by medium size Embraer aircraft); increasing use of a hub-and-spoke network strategy; introduction of business lounges and premium seating classes; code-sharing and/or interlining arrangements with domestic (for example, REX Express) and international airlines, such as Air New Zealand, Delta Airlines, Etihad and Hawaiian Airlines), and a mixed fleet, including long-haul Boeing 777 aircraft used to operate services to the USA) (Knibb 2008b; Koo 2009). In December 2012, Virgin Australia announced plans to match the Qantas portfolio of domestic airlines by acquiring 60 per cent of Tiger Airways Australia and all of Perth, Western Australia-based Skywest Airlines. Under this strategy Tiger Airways would compete on Virgin’s behalf against Qantas LCCs unit Jetstar, whilst Skywest would compete against QantasLink on regional and mining-related routes. In order to fund these acquisitions, and the cost of enhancing these airlines operations, Virgin sold a ten per cent stake in itself to Singapore Airlines. This ambitious initiative formed part of Virgin Australia’s re-branding strategy, designed to distance itself from the low-cost carrier sector and compete more with mainline Qantas in the premium business market (Knibb 2012).
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Air Travel Demand. IATA ECONOMICS BRIEFING N o 9. Measuring the responsiveness of air travel demand to changes in prices and incomes

Air Travel Demand. IATA ECONOMICS BRIEFING N o 9. Measuring the responsiveness of air travel demand to changes in prices and incomes

Route Level. The review of previous research found route level elasticities ranging from -1.2 to -1.5. Regressions using the US DB1A data, which allows the use of route dummies and variables to capture the price of route substitutes, produced a similar air travel price elasticity of -1.4. This elasticity estimate is applicable to a situation where the price of an individual route changes (e.g. higher airport charges at Paris CDG raising the price of travel from London and diverting leisure traffic to another destination, such as Frankfurt). Using distance as an instrument variable within the 2SLS model produces results that further support this elasticity, though there still is some concern over the use of distance in this way due to its perceived exogenous influence on demand. National Level. Estimates of national elasticities using all three datasets found that, without the route substitution term, elasticities fell to around -0.8. This inelastic result was found over a range of model specifications which excluded route dummies. The national level elasticity applies to a situation such as the doubling of a national
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Modelling the Demand for Money in Pakistan

Modelling the Demand for Money in Pakistan

This section presents the outcome of second step of the methodology. This step consists of two parts, that is, the test of cointegration and estimation of long run money demand function. To test for cointegration, the Johansen Likelihood Ratio Tests based on trace and maximum eigenvalue statistics are applied. We use different combinations of variables and different order of VAR. Final selection is made on the basis of error term that became white noise and the cointegrating vector gives theoretically plausible results. The lag selection criteria such as AIC and SBC are also used for this purpose. Similarly, the final versions of the ECM of demand for M2 are reported here which is selected on the basis of diagnostic tests. For all the test statistics we use 5 percent level of significance unless otherwise stated.
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A Long-Distance Travel Demand Model for Europe

A Long-Distance Travel Demand Model for Europe

DTU Transport, Technical University of Denmark, Denmark I n Europe, approximately 50% of all passenger kilometres come from trips beyond 100 km according to matrices developed in the TRANSTOOLS project. This accounts for an even larger share of CO2 emissions due to a higher modal share of air transport. Therefore long-distance trips are increasingly relevant from a political and environmental point of view. The paper presents the first tour-based long-distance travel demand model for passenger trips in and between 42 European countries. The model is part of a new European transport model developed for the European Commission, the TRANSTOOLS II model, and will serve as an important tool for transport policy analysis at a European level. The model is formulated as a nested logit model and estimated based on travel diary data with segmentation into business, private, and holiday trips. We analyse the estimation results and present elasticities for a number of different level-of- service variables. The results suggest that the perception of both travel time and cost varies with journey length in a non-linear way. For car drivers and car passengers, elasticities increase with the length of the journey, whereas the opposite is true for rail, bus, and air passengers – a fact that reflects a change in substitutability. Moreover, elasticities differ significantly by trip purpose with private trips having the highest and holiday trips the lowest elasticities.
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COMPARISON OF AIR TRAVEL DEMAND FORECASTING METHODS

COMPARISON OF AIR TRAVEL DEMAND FORECASTING METHODS

Accurate forecasts of future passenger demand are essential to effective revenue management system. The seat inventory control leans on predictions about the bookings to come to optimally allocate aircraft seats among the various booking classes. Forecasting for airline revenue management systems is inherently difficult because of complex nature of air travel demand which is highly stochastic. The problem is further complicated because of usually great number of origin destination pairs, each with its own seasonal and weekly effects, the economic environment and external factors like competition or special events. The paper describes general problem of forecasting airline demand and compares traditional methods of forecasting (moving averages, exponential smoothing, etc.) against neural networks as a forecasting method. All the methods are compared on the basis of standard error measures.
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Modelling the potential benefits increased active travel

Modelling the potential benefits increased active travel

Cyclists’ Federation, 2017; Pucher and Buehler, 2017). There has also been significant research in recent years to explore methods to improve cycling conditions and increase usage. Abraham et al. (2002) assessed the attractiveness of a cycling in Calgary, using a SP survey, in response to proposed improvements being made to travel times on different categories of cycling facilities (i.e. shared and segregated cycle lanes), in addition to the availability of cycle-friendly facilities at the destination (e.g. secure parking and showers/ locker rooms). The results from this study found that while cyclists prefer routes that offer short trip times, cyclists would also be prepared to travel longer distances on routes providing proper cycling infrastructure and destination amenities. In other words, cyclists would be willing to make a trade- off between shorter trip times and the incidence of improved cycling infrastructure. Conversely, results produced by Pooley et al. (2013) determined that safety concerns, familial responsibilities and social perceptibility of cycling by family and peers were prime reasons negatively affecting the likelihood of choosing cycling as a mode.
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Modelling of water demand in distribution networks

Modelling of water demand in distribution networks

The concept of uniformly distributed loads, and of upper bound solutions, that are familiar in structural engineering, may prove to be useful ideas when considering a water distribution network. Demand may be considered as uniformly distributed along pipelines, for comparison with results obtained from point demands applied at nodes. The various ways of idealising the system to apply nodal demands may be compared, and it will be shown that these represent upper bound or unsafe estimates in relation to the true solution.

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Integration of Travel Demand Models With Operational Analysis Tools

Integration of Travel Demand Models With Operational Analysis Tools

Challenges include limited financial resources, environmental constraints, adverse impacts from the displacement of businesses and residences, and increased costs associated with urban work zones. Accordingly, operational strategies, such as the provision of travel information; the management of signal timings, and ramp metering, have received attention as they tend to have fewer adverse impacts and a shorter deployment time than traditional construction alternatives. Yet fully considering these alternatives within the planning process requires their evaluation at a high level of specificity as would be the case with a capacity expansion. For example, the goal of a traveler information system may be to reduce secondary incidents associated with routine queuing (e.g., a queue warning system on a freeway) or the goal may be to elicit a change in departure time (e.g., a web-based trip planning system). For each goal, the impact of the system on measures of effectiveness (MOEs) such as reliability (e.g., travel time variance), delay (e.g., vehicle hours of travel), safety (e.g., number of crashes), and air quality (e.g., pounds of nitrogen oxides) will need to be forecast so that the impact can be compared with a range of other
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Empirical Modelling of the Mobile VoIP Demand

Empirical Modelling of the Mobile VoIP Demand

The actual adoption of mobile VoIP depends on many things, as depicted in the theoretical model of the figure above. First of all, there should be the demand for mobile voice. This is probably the most certain thing of all. In addition, mobile broadband penetration should go up. Mobile VoIP requires mobile packet access connectivity. Correct pricing, marketing and technical implementation of mobile packet access is inevitable. In addition, diffusion of mobile broadband equipped handsets is needed for mobile VoIP to fly. Diffusion of 3G capable handsets, devices with WiFi connectivity embedded, and hotspot networks, for example, are required for sufficient mobile broadband diffusion. In addition to mere packet data access diffusion, also suitable applications and services are required. This includes the development and introduction of mobile VoIP clients and software. The deployment of SIP (for standardized SIP mobile VoIP) or support for third party applications in smartphones (for applications such as mobile Skype) should
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Study of Urban Travel Demand Characteristics of Twin City

Study of Urban Travel Demand Characteristics of Twin City

A traffic situation and requirement of transport planning and development of infrastructure, economic growth and spatial developments are quite often governed by the quality and quantity of transport infrastructure provided. Before undertaking such projects, it will be necessary to carry out a detailed analysis of the traffic and transportation situation and prepare long term strategies and plans to establish the requirement and viability of such projects. It will be appropriate to prepare a comprehensive mobility plan that reflects the land use changes as well as the changes in the economy and industrialization. This project is to suggest a suitable Bus rapid system for a twin city by the analysis of travel demand and its travelling pattern.
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Modelling money demand in the FYR of Macedonia

Modelling money demand in the FYR of Macedonia

The empirical analysis of money demand seems to be of crucial importance for checking the existence of a stable relationship between money and prices as a prerequisite of attaching a greater role for monetary aggregates in the conduct of monetary policy. This paper provides for an empirical analysis of the demand for narrow money in the FYR of Macedonia. Specifically, the paper deals with the following issues: the empirical modelling of the demand for money in the long-run, the short-run dynamics of money, and the stability of the demand for money.
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Characterisation and Modelling of Residential Electricity Demand

Characterisation and Modelling of Residential Electricity Demand

The possible privacy issues related to the detection and monitoring of appliances have been assessed as well: ‘How much extra information, e.g. income, can be derived from monitoring systems?’ Distribution system operators will monitor the electricity demand at the point of common coupling of houses with smart meters. Detecting appliances in those measurements is only possible if the resolution is high enough, for example 6 seconds. Appliances are hard to detect on a fifteen minute time scale, the resolution of the smart meters. Hence, distribution system operators will have difficulty in detecting appliances. Providers of home management systems, on the other hand, monitor the electricity demand of individual appliances as well as the electricity demand at the connection point on a fifteen minute scale. The cycle detection and settings estimation algorithms for washing machines, tumble dryers and dishwashers presented allow for the estimation of privacy sensitive information, such as income and family expansion.
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Rough sets theory for travel demand analysis in Malaysia

Rough sets theory for travel demand analysis in Malaysia

Articles on tourism demand modelling incorporate up-to-date developments in econometric methodology have reached conflicting conclusions in terms of the methods that generate the most accurate forecasts. For example, Kulendran & King (1997) and Kulendran & Witt (2001) found that economic models were still outperformed by simple univariate time series models. By contrast, Kim and Song (1998) and Song, Romilly and Liu (2000) found that the forecasting performance of econometric models was superior to simple time series models.

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The Changing Price Elasticity of Demand for Domestic Airline Travel

The Changing Price Elasticity of Demand for Domestic Airline Travel

Consumers make economic decisions as to what they buy based largely on price. More specifically, the change in the amount of a good purchased is often highly dependent on its change in price. That measure of responsiveness is defined as the price elasticity of demand. Mathematically, it is often expressed as: Ed= - percent change in quantity demanded / percent change in price, or -(dQ/Q)/(dP/P). The minus sign is often omitted because price elasticity of demand is presumed to be negative. If E d = 0, it is perfectly inelastic, a change in price does not affect the quantity demanded. If 0 >Ed>-1, it is relatively inelastic, the quantity demanded does not increase at the same rate the price falls. If Ed = -1, there is unitary elasticity, both price and demand change equally. If -1>E d , it is elastic, demand increases more than the fall in price. It is presumed that the changes in price are small. If the price elasticity of demand is not greater (more negative) than -1, a drop in price can actually reduce overall revenue received by the seller.
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