Forecasting current and next trip purpose with social media data and Google Places

Trip purpose is crucial to travel behavior modeling and travel demand estimation for transportation planning and investment decisions. However, the spatial-temporal complexity of human activities makes the prediction of trip purpose a challenging problem. This research, an extension of work by Ermagun et al. (2017) and Meng et al. (2017), addresses the problem of predicting both current and next trip purposes with both Google Places and social media data. First, this paper implements a new approach to match points of interest (POIs) from the Google Places API with historical Twitter data. Therefore, the popularity of each POI can be obtained. Additionally, a Bayesian neural network (BNN) is employed to model the trip dependence on each individual’s daily trip chain and infer the trip purpose. Compared with traditional models, it is found that Google Places and Twitter information can greatly improve the overall accuracy of prediction for certain activities, including “EatOut”, “Personal”, “Recreation” and “Shopping”, but not for “Education” and “Transportation”. In addition, trip duration is found to be an important factor in inferring activity/trip purposes. Further, to address the computational challenge in the BNN, an elastic net is implemented for feature selection before the classification task. Our research can lead to three types of possible applications: activity-based travel demand modeling, survey labeling assistance, and online recommendations.     

The estimation of a time-dependent OD trip table with vehicle trajectory samples

Abstract

In this paper we discuss a dynamic origin–destination (OD) estimation problem that has been used for identifying time-dependent travel demand on a road network. Even though a dynamic OD table is an indispensable data input for executing a dynamic traffic assignment, it is difficult to construct using the conventional OD construction method such as the four-step model. For this reason, a direct estimation method based on field traffic data such as link traffic counts has been used. However, the method does not account for a logical relationship between a travel demand pattern and socioeconomic attributes. In addition, the OD estimation method cannot guarantee the reliability of estimated results since the OD estimation problem has a property named the ‘underdetermined problem.’ In order to overcome such a problem, the method developed in this paper makes use of vehicle trajectory samples with link traffic counts. The new method is applied to numerical examples and shows promising capability for identifying a temporal and spatial travel demand pattern.     

Welfare evaluation with a road capacity constraint

Severe traffic congestion in and around many cities across the world has resulted in programmes of extensive road building and other capacity increasing projects. But traffic congestion has often not fallen in the long run and neither has journey speed increased. Demand for peak period road travel, particularly by car, has grown so strongly that increases in road capacity have been quickly matched by increased road use. This paper develops a model of a road network characterised by insatiable road passenger (car and bus) demand. The model parameters are calibrated on a typical urban road network, and a number of simulations conducted to determine social welfare after the introduction of a road capacity constraint into the optimisation process. The empirical results have an important policy implication for the evaluation of projects that increase road capacity, namely that standard methods of cost-benefit analysis may tend to overestimate the net benefits of such projects by a significant amount. Although the model is developed in the context of roads and road traffic congestion, it could also be applied to air travel.     

Modelling rain effects on risk‐taking behaviours of multi‐user classes in road networks with uncertainty

This paper proposes a new travel time reliability‐based traffic assignment model to investigate the rain effects on risk‐taking behaviours of different road users in networks with day‐to‐day demand fluctuations and variations in travel time. A generalized link travel time function is used to capture the rain effects on vehicle travel times and road conditions. This function is further incorporated into daily demand variations to investigate those travel time variations arising from demand uncertainty and rain condition. In view of these rain effects, road users' perception errors on travel times and risk‐taking behaviours on path choices are incorporated in the proposed model with the use of a logit‐based stochastic user equilibrium framework. This new model is formulated as a variational inequality problem in terms of path flows. A numerical example is used to illustrate the application of the proposed model for assessment of the rain effects on road networks with uncertainty.     

Estimating origin‐destination matrices from link volume counts: a simple intercity auto travel demand model for Sri Lanka

The cost of nation wide travel surveys is high. Hence in many developing countries, planners have found it difficult to develop intercity transportation plans due to the non availability of origin‐destination trip matrices. This paper will describe a method for the intercity auto travel estimation for Sri Lanka with link traffic volume data.

The paper outlines the rationale of selecting the district capitals of Sri Lanka as its “cities,” the methodology for selecting the intercity road network, determination of link travel times from express bus schedules and the location of link volume counting positions.

Initially, the total auto travel demand model is formulated with various trip purpose sub‐models. This model is finally modified to a simple demand model with district urban population and travel times between city pairs as the exogenous variables, to overcome statistical estimation difficulties. The final demand model has statistics within the acceptable regions.

The advantages of a simple model are discussed and possible extensions are proposed.     

Testing the stability of travel expenditures in Nigeria

A report is presented on a study carried out to develop a functional form for travel money expenditure in a Nigerian setting, and test its stability against energy policy change, specifically the fuel price increase of October 1994. The Box–Cox transformation regression approach was adopted in the modelling exercise in order to evolve a data-defined functional form and ensure a more rational basis for the stability test. The results of the modelling exercise show that while statistically significant functional forms were estimated for the “before” and “after” fuel price increase periods, the functional forms estimated are not stable across the periods. Thus “travel budget” is as yet not usable as a term for travel expenditures in Nigeria. The implication of this for travel demand modelling in Nigeria is that, at least till other evidences prove otherwise, there is as yet no basis for using the “Universal Mechanism Of Travel” model developed by Zahavi (The UMOT Project. Report No. DOT-RSPA-DPB-20-79-3; The UMOT Travel Model II Report No. DOT-RSPA-DPB-50-82-11). Of disposable income and total expenditure, the former has proved to be more appropriate for use as “available money” for the estimation of travel expenditures in Nigeria in the “before” energy policy change period, while total expenditure proved appropriate in the “after” period.     

Impacts of highly automated vehicles on travel demand: macroscopic modeling methods and some results

Automated vehicles (AV) will change transport supply and influence travel demand. To evaluate those changes, existing travel demand models need to be extended. This paper presents ways of integrating characteristics of AV into traditional macroscopic travel demand models based on the four-step algorithm. It discusses two model extensions. The first extension allows incorporating impacts of AV on traffic flow performance by assigning specific passenger car unit factors that depend on roadway type and the capabilities of the vehicles. The second extension enables travel demand models to calculate demand changes caused by a different perception of travel time as the active driving time is reduced. The presented methods are applied to a use case of a regional macroscopic travel demand model. The basic assumption is that AV are considered highly but not fully automated and still require a driver for parts of the trip. Model results indicate that first-generation AV, probably being rather cautious, may decrease traffic performance. Further developed AV will improve performance on some parts of the network. Together with a reduction in active driving time, cars will become even more attractive, resulting in a modal shift towards car. Both circumstances lead to an increase in time spent and distance traveled.

     

Fuel demand,road transport pollution emissions and residents’ health losses in the transitional China

In recent years, China’s rapid economic growth resulted in serious air pollution, which caused substantial losses to economic development and residents’ health. In particular, the road transport sector has been blamed to be one of the major emitters. During the past decades, fluctuation in the international oil prices has imposed significant impacts on the China’s road transport sector. Therefore, inspired by Li and Zhou (2005), we propose an assumption that China’s provincial economies are independent “economic entities”. Based on this assumption, we investigate the China’s road transport fuel (i.e., gasoline and diesel) demand system by using the panel data of all 31 Chinese provinces except Hong Kong, Macau and Taiwan. To connect the fuel demand system and the air pollution emissions, we propose the concept of pollution emissions elasticities to estimate the air pollution emissions from the road transport sector, and residents’ health losses by a simplified approach consisting of air pollution concentrations and health loss assessment models under different scenarios based on real-world oil price fluctuations. Our framework, to the best of our knowledge, is the first attempt to address the transmission mechanism between the fuel demand system in road transport sector and residents’ health losses in the transitional China.     

Capturing value of reliability through road pricing in congested traffic under uncertainty

Empirical studies showed that travel time reliability, usually measured by travel time variance, is strongly correlated with travel time itself. Travel time is highly volatile when the demand approaches or exceeds the capacity. Travel time variability is associated with the level of congestion, and could represent additional costs for travelers who prefer punctual arrivals. Although many studies propose to use road pricing as a tool to capture the value of travel time (VOT) savings and to induce better road usage patterns, the role of the value of reliability (VOR) in designing road pricing schemes has rarely been studied. By using road pricing as a tool to spread out the peak demand, traffic management agencies could improve the utility of travelers who prefer punctual arrivals under traffic congestion and stochastic network conditions. Therefore, we could capture the value of travel time reliability using road pricing, which is rarely discussed in the literature. To quantify the value of travel time reliability (or reliability improvement), we need to integrate trip scheduling, endogenous traffic congestion, travel time uncertainty, and pricing strategies in one modeling framework. This paper developed such a model to capture the impact of pricing on various costs components that affect travel choices, and the role of travel time reliability in shaping departure patterns, queuing process, and the choice of optimal pricing. The model also shows the benefits of improving travel time reliability in various ways. Findings from this paper could help to expand the scope of road pricing, and to develop more comprehensive travel demand management schemes.     

Transport Network Design Problem under Uncertainty: A Review and New Developments

This paper aims to provide a state-of-the-art review of the transport network design problem (NDP) under uncertainty and to present some new developments on a bi-objective-reliable NDP (BORNDP) model that explicitly optimizes the capacity reliability and travel time reliability under demand uncertainty. Both are useful performance measures that can describe the supply-side reliability and demand-side reliability of a road network. A simulation-based multi-objective genetic algorithm solution procedure, which consists of a traffic assignment algorithm, a genetic algorithm, a Pareto filter, and a Monte-Carlo simulation, is developed to solve the proposed BORNDP model. A numerical example based on the capacity enhancement problem is presented to demonstrate the tradeoff between capacity reliability and travel time reliability in the NDP.     

Impacts of highway congestion on freight operations: perceptions of trucking industry managers

To better understand how road congestion adversely affects trucking operations, we surveyed approximately 1200 managers of all types of trucking companies operating in California. More than 80% of these managers consider traffic congestion on freeways and surface streets to be either a “somewhat serious” or “critically serious” problem for their business. A structural equations model (SEM) is estimated on these data to determine how five aspects of the congestion problem differ across sectors of the trucking industry. The five aspects were slow average speeds, unreliable travel times, increased driver frustration and morale, higher fuel and maintenance costs, and higher costs of accidents and insurance. The model also simultaneously estimates how these five aspects combine to predict the perceived overall magnitude of the problem. Overall, congestion is perceived to be a more serious problem by managers of trucking companies engaged in intermodal operations, particularly private and for-hire trucking companies serving airports and private companies serving rail terminals. Companies specializing in refrigerated transport also perceive congestion to be a more serious overall problem, as do private companies engaged in LTL operations. The most problematic aspect of congestion is unreliable travel times, followed by driver frustration and morale, then by slow average speeds. Unreliable travel times are a significantly more serious problem for intermodal air operations. Driver frustration and morale attributable to congestion is perceived to be more of a problem by managers of long-haul carriers and tanker operations. Slow average speeds are also more of a concern for airport and refrigerated operations.     

Profit maximization by a private toll road with cars and trucks

This paper examines the profit maximizing behavior of a private firm which operates a toll road competing against a free alternative in presence of cars and trucks. Trucks differ from cars in value of time (VOT), congestion externality, pavement damage, and link travel time function. We find that the firm takes either a car-strategy or a truck-strategy for profit maximization. For a traffic mix with relatively large car volume and small truck volume, the car-strategy results in no trucks using the toll road, while the truck-strategy results in all trucks using the toll road. We derive the equilibrium flow pattern under any combination of car-toll and truck-toll, based on which we identify a profit-maximizing frontier and a strategy-switching frontier in the car-toll and truck-toll two-dimensional space. By geometrically comparing the two frontiers, we establish general conditions under which each strategy will be taken, which suggest that the truck-to-car VOT ratio, the total traffic demand, and the difference in travel distance between the two roads are critical in shaping the firm's strategy.     

Profit-based maritime container assignment models for liner shipping networks

We propose the problem of profit-based container assignment (P-CA), in which the container shipment demand is dependent on the freight rate, similar to the “elastic demand” in the literature on urban transportation networks. The problem involves determining the optimal freight rates, the number of containers to transport and how to transport the containers in a liner shipping network to maximize the total profit. We first consider a tactical-level P-CA with known demand functions that are estimated based on historical data and formulate it as a nonlinear optimization model. The tactical-level P-CA can be used for evaluating and improving the container liner shipping network. We then address the operational-level P-CA with unknown demand functions, which aims to design a mechanism that adjusts the freight rates to maximize the profit. A theoretically convergent trial-and-error approach, and a practical trial-and-error approach, are developed. A numerical example is reported to illustrate the application of the models and approaches.     

Travel mental budgeting under road toll: An investigation based on user equilibrium

With the approach of introducing the conceptions of mental account and mental budgeting into the process of travelers’ route choice, we try to identify why the usages of tolled roads are often overestimated. Assuming that every traveler sets a mental account for his/her travel to keep track of their expense and keep out-of-pocket spending under control, it addresses these questions such that “How much money can I spend on the travel?” and “What if I spend too much?”. Route tolls that exceed the budget are much more unacceptable compared to those within budget due to the non-fungibility of money between different accounts. A simple network with two nodes and two routes is analyzed firstly, the analytical solutions are obtained and the optimal road tolls supporting the user equilibrium as a system optimum are also derived. The proposed model is then extended to a generalized network. The multiclass user equilibrium conditions with travel mental budgeting are formulated into an equivalent variational inequality (VI) problem and an equivalent minimization problem. Through analyses with numerical examples, it is found that the main reason that the usages of high tolled roads are often overestimated is due to the fact that travelers with low and moderate out-of-pocket travel budget perceive a much higher travel cost than their actual cost on the high tolled roads.     

Asking the right questions about telecommuting: Avoiding pitfalls in surveying homebased work

In designing travel behavior surveys, the problem is to define “work,” “home,” and similar words that are commonly used in our language but which have acquired a plethora of associated meanings. The difficulty has not been resolved by the many new terms coined to describe non-traditional ways to work. Such words as “telecommuting,” “teleworking,” “at-home work,” “hoteling,” “homebased business,” “road warriors” and “mobile workers,” lack any agreed-upon definitions yet they are used in common parlance as if they did. These new workstyles are of interest to travel planners because they may involve trip reduction. To forecast just how much trip reduction will occur, behavior needs to be measured by objective criteria. To avoid definitional traps, we recommend phrasing questions in terms of measurable variables such as the place of work and the time in days and hours spent at each location. That approach leaves researchers the option of applying their own definitions that fit the context of their analyses. Thus, rather than ask “How many days a week do you telecommute?” the more precise question can be asked: “How many days last week did you work at home instead of going to your usual work location?” This approach has the advantage that information gathered over years can be used unambiguously in various contexts. Definitions can be applied at the point of analysis. This paper illustrates errors and confusion that can arise from casually worded surveys using examples from private and public surveys. The author proposes a set of core questions with four levels of priority for consideration in designing future surveys of travel behavior.     

Dynamic pricing for managed lanes with multiple entrances and exits

Priced managed lanes are increasingly being used to better utilize the existing capacity of the roadway to relieve congestion and offer reliable travel time to road users. In this paper, we investigate the optimization problem for pricing managed lanes with multiple entrances and exits which seeks to maximize the revenue and minimize the total system travel time (TSTT) over a finite horizon. We propose a lane choice model where travelers make online decisions at each diverge point considering all routes on a managed lane network. We formulate the problem as a deterministic Markov decision process and solve it using the value function approximation (VFA) method for different initializations. We compare the performance of the toll policies predicted by the VFA method against the myopic revenue policy which maximizes the revenue only at the current timestep and two heuristic policies based on the measured densities on the managed and general purpose lanes (GPLs). We test the results on four different test networks. The primary findings from our research suggest the usefulness of the VFA method for determining dynamic tolls. The best-found objective value from the method at its termination is better than other heuristics for all test networks with average improvements in the objective ranging between 10% and 90% for revenue maximization and 0–27% for TSTT minimization. Certain VFA initializations obtain best-found toll profiles within first 5–50 iterations which warrants computational time savings. Our findings also indicate that the revenue-maximizing optimal policies follow the “jam-and-harvest” behavior where the GPLs are pushed towards congestion in the earlier time steps to generate higher revenue in the later time steps, a characteristic not observed for the policies minimizing TSTT.     

Road pricing for congestion control with unknown demand and cost functions

It is widely recognized that precise estimation of road tolls for various pricing schemes requires a few pieces of information such as origin–destination demand functions, link travel time functions and users’ valuations of travel time savings, which are, however, not all readily available in practice. To circumvent this difficulty, we develop a convergent trial-and-error implementation method for a particular pricing scheme for effective congestion control when both the link travel time functions and demand functions are unknown. The congestion control problem of interest is also known as the traffic restraint and road pricing problem, which aims at finding a set of effective link toll patterns to reduce link flows to below a desirable target level. For the generalized traffic equilibrium problem formulated as variational inequalities, we propose an iterative two-stage approach with a self-adaptive step size to update the link toll pattern based on the observed link flows and given flow restraint levels. Link travel time and demand functions and users’ value of time are not needed. The convergence of the iterative toll adjustment algorithm is established theoretically and demonstrated on a set of numerical examples.     

Adaptive traffic signal control with equilibrium constraints under stochastic demand

This study develops a methodology to model transportation network design with signal settings in the presence of demand uncertainty. It is assumed that the total travel demand consists of commuters and infrequent travellers. The commuter travel demand is deterministic, whereas the demand of infrequent travellers is stochastic. Variations in demand contribute to travel time uncertainty and affect commuters’ route choice behaviour. In this paper, we first introduce an equilibrium flow model that takes account of uncertain demand. A two-stage stochastic program is then proposed to formulate the network signal design under demand uncertainty. The optimal control policy derived under the two-stage stochastic program is able to (1) optimize the steady-state network performance in the long run, and (2) respond to short-term demand variations. In the first stage, a base signal control plan with a buffer against variability is introduced to control the equilibrium flow pattern and the resulting steady-state performance. In the second stage, after realizations of the random demand, recourse decisions of adaptive signal settings are determined to address the occasional demand overflows, so as to avoid transient congestion. The overall objective is to minimize the expected total travel time. To solve the two-stage stochastic program, a concept of service reliability associated with the control buffer is introduced. A reliability-based gradient projection algorithm is then developed. Numerical examples are performed to illustrate the properties of the proposed control method as well as its capability of optimizing steady-state performance while adaptively responding to changing traffic flows. Comparison results show that the proposed method exhibits advantages over the traditional mean-value approach in improving network expected total travel times.     

Accuracy of distance‐based travel time decomposition in probe vehicle systems

Probe vehicle data (PVD) are commonly used for area‐wide measurements of travel time in road networks. In this context, travel times usually refer to fixed edges of an underlying (digital) map. That means measured travel times have to be transformed into so‐called link travel times first. This paper analyzes a common method being applied for solving this task (distance‐based travel time decomposition). It is shown that, in general, its inherent imprecision must not be neglected. Instead, it might cause a serious misinterpretation of data if potential errors in the context of travel time decomposition are ignored. For this purpose, systematic as well as maximum deviations between “decomposed” and “true” link travel times are mathematically analyzed. By that, divergent statements in the literature about the accuracy of PVD are harmonized. Moreover, conditions for the applicability of the so‐called distance‐proportion method are derived depending on the permitted error level. Three examples ranging from pure theory to real world confirm the analytical findings and underline the problems resulting from distance‐based travel time decomposition at local level, for example, at individual intersections. Copyright © 2013 John Wiley & Sons, Ltd.     

Road pricing: some further comments

This paper extends the discussion of certain problems associated with road pricing raised in an earlier contribution to this journal. Firstly it is shown that where the value of time varies between different groups of traffic using the same congested road, optimal road pricing requires price discrimination between the groups such that those with the lowest value of time pay the highest charge and suffer the greatest losses. However, a uniform price based on an “equity” value of time would reduce the relative distributional effects. Secondly the paper takes up the suggestion that queueing (and by extension congestion) may be positively helpful in the allocation of resources and shows that this is not in general correct although there are situations in which the use of queueing alongside conventional pricing may have a role to play. Finally it is argued that governments’ apparent lack of enthusiasm for road pricing stems as much from political considerations as the more technical problems of applying it.