Equilibrium Trip Scheduling in Congested Traffic under Uncertainty

Predominantly, existing dynamic traffic assignment studies presume that travel time is deterministic, merely subjected to congestion due to capacity limitations. In view of the inevitability of travel-time uncertainty, a lot of effort has been spent to investigate how uncertainty affects travel choices. Extending from the bottleneck scheduling model, this article establishes the connection between trip scheduling and punctuality reliability by considering that travellers value earliness and lateness differently according to their different degrees of punctuality reliability. Punctuality reliability refers to the probability of being not late for a scheduled activity, which is heterogeneous among travellers and depends on their degrees of risk aversion. By incorporating the notion of punctuality reliability, we can produce the sensible result that risk-averse travellers (those with higher punctuality reliability) choose to depart from home at earlier times, while such a mapping or feature is absent in the original model by Small [1982. The scheduling of consumer activities: work trips. American Economic Review, 72, 467–479]. The proposition is confirmed by our empirical study. The modelling framework is then demonstrated numerically first to the scheduling problem in a single bottleneck then to parallel bottlenecks that offer route choices.

Quantifying errors in travel time and cost by latent variables

The paper develops a dynamic model of peak period traffic congestion that considers a limited number of bottlenecks. The model predicts the temporal distribution of traffic volumes with an elastic demand model. In response to changes in the traffic conditions travelers can switch to a different mode, divert to an alternate route, or shift the trip forward or backward in time to avoid a long delay. A simple example would be the case of two parallel routes with travelers jointly selecting route and departure time. The choice of route and mode are dependent on travel times and travel costs. The choice of departure time is based on the trade-off between travel time and schedule delay which is the difference between the actual and the desired arrival times. The delays at the bottlenecks are modeled with a deterministic queueing model that determines waiting time as a function of the length of the queue at the time of arrival at the bottleneck. The day to day adjustment of the distribution of traffic is derived from a dynamic Markovian model. The model is used to perform simulation experiments. The results demonstrate the response of the traffic conditions at the bottlenecks to a change in the system. The model is used to analyze the impacts of alternative pricing policies and preferential treatment of high occupancy vehicles.

Distribution centres (DCs) are the hubs connecting transport streams in the supply chain. The synchronisation of coming and going cargo at a DC requires reliable arrival times. To achieve this, a reliable method to predict arrival times is needed. A literature review was performed to find the factors that are reported to predict arrival time: congestion, weather, time of day and incidents. While travel time receives considerable attention, there is a gap in literature concerning arrival vs. travel/journey time prediction. None of the reviewed papers investigate arrival time: all the papers found investigate travel time. Arrival time is the consequence of travel time in combination with departure time, so though the travel time literature is applicable, the human factor involved in planning the time of departure can affect the arrival time (especially for truck drivers who have travelled the same route before). To validate the factors that influence arrival time, the authors conducted a detailed case study that includes a survey of 230 truckers, a data analysis and a data mining experiment, using real traffic and weather data. These show that although a ‘big data’ approach delivers valuable insights, the predictive power is not as high as expected; other factors, such as human or organisational factors, could influence arrival time, and it is concluded that such organisational factors should be considered in future predictive models.

Route swapping in dynamic traffic networks

In MOH pharmacies, prescriptions for chronic medications are partially dispensed with a supply duration of 1 month, to ensure the quality use of medicines. Monthly visits may impose nontrivial travel time and costs on patients. This study aimed to assess travel time and costs to collect repeat prescriptions at district hospitals in Sabah. A cross-sectional study, with 310 respondents was recruited using convenient sampling, over 3 months (April to June 2019). The questionnaire was adapted from a study conducted in Denmark, which was pilot-tested before the main study. Data collected were analyzed using SPSS (version 18) and presented using descriptive statistics. A statistically significant difference was defined at p-value < 0.05. 310 participants responded in 6 district hospitals (Beluran Hospital, Kinabatangan Hospital, Beaufort Hospital, Kuala Penyu, Hospital, Ranau Hospital, Pitas Hospital) in Sabah. The results of this study suggested that for patients collecting repeat prescriptions at government hospitals in Sabah, the median travelling cost for a return trip was RM 8.00 (IQR RM 11.00), while the median travelling time for a return trip was 30 minutes (IQR 45 minutes). Overall, the reported median cost and time spent to collect repeat prescriptions were not surprising. Among the problems faced were the unavailability of a method to validate the travel time and cost estimates, and it was suspected that some respondents might have a lack of knowledge regarding travel costs. However, the detailed data on travel time fits well with expectations based on the relatively sparse distribution of healthcare facilities within a relatively large state of Sabah. This study was not powered to detect the social impact on patients in marginalized groups (high cost and lengthy time of travel), and future studies should focus on this section of the population, to review the current policy for this group of patiens.

Cross-nested logit model for the joint choice of residential location, travel mode, and departure time

Transportation is one of the problems that becomes a measure of the success of a city. Bekasi City, as the gateway and buffer for the capital city of Jakarta, has high mobility due to the density of the city of Jakarta. This study aims to determine the characteristics and factors that influence mode selection, mode selection models that can explain probabilities, as well as people's preferences in choosing the Commuter Line Electric Train (KRL) and Transjakarta Bus transportation modes used by the people of Bekasi City to East Jakarta. Data was collected using a questionnaire survey using the Stated Preference method, distributed offline and online. Furthermore, the data from the survey were processed using regression analysis to evaluate the relationship between the response variable, namely the choice of travelers on a numerical scale (Y), with changes in the analyzed attributes, namely the difference in travel costs (X1), travel time (X2), and the departure time of each mode (X3). The results of the linear regression analysis using the SPSS version 28 program obtained the mode selection model, namely (UMRT – UTJ) = 0.662 + 0.044.X1 - 0.014.X2 + 0.025.X3. The conclusion obtained from the results of this study is that the KRL Commuter Line is the preferred mode of transportation compared to the Transjakarta Bus with the Bekasi - East Jakarta route, and the travel cost attribute is the most influential in the transportation mode selection model.

Sensitivity analysis for transit equilibrium assignment and applications to uncertainty analysis

Potential uptake of adaptive transport services: An exploration of service attributes and attitudes

270 Background: Cancer creates a tremendous financial burden. Cancer-related costs are categorized into direct, indirect and psychosocial costs. Although there have been many reports on medical care costs, which are direct, those on other costs are extremely scarce. We estimated travel time and costs required for cancer patients to receive outpatient treatment. Methods: We studied 524 cancer patients given anti-cancer treatment between February 2009 and December 2012 at the Outpatient Chemotherapy Center of Teikyo University Chiba Medical Center. Address data were extracted from Data Warehouse electronic medical records, and travel distance and time required for outpatient treatment were calculated via MapInfo and ACT Distance Calculator Package. Transportation costs were estimated on the basis of ¥274 (= $3.00) per kilometer. Results: Average round-trip travel distances, times, and costs by cancer type are shown below. Average round-trip travel distance, time, and cost for all patients were 26.7 km, 72.5 minutes, and ¥7,303 ($79.99), respectively. Conclusions: Cancer patients incurred a travel cost of approximately ¥4,000 to ¥9,000 ($40.00 to $100.00) for each outpatient treatment. With population aging, seniors living alone and senior households are increasing, and outpatient visits are becoming a common burden. [Table: see text]

Effects of Friends' Information Interaction on Travel Decisions

With the increases in both the urban rail transit (URT) network scale and complexity, a route choice model that was previously developed may not function properly anymore and therefore must be constantly evaluated if possible and updated whenever necessary. This paper develops and uses a posterior approach that fuses multisource data from both the automatic fare collection (AFC) and automatic train supervision (ATS) systems to provide accurate and intelligent evaluation of route choice models, especially for large-scale complex URT networks. A method to rebuild passengers' journey one by one is put forward that makes the proposed approach work in a more disaggregate manner. Then, observed travel time (OTT), and simulated travel time (STT), which are deduced by fusing multisource data from AFC and ATS systems, are defined. Instead of using traditional manual-based methods, the evaluation of route choice models is conducted by comparing and testing the distributions of both OTTs and STTs, and two nonparametric statistical techniques (NPSTs) are adopted. Pilot case studies are conducted on the Beijing subway network and the results obtained clearly show that the approach can disaggregately evaluate the route choice model and can also be easily incorporated into an automatic evaluation procedure.

In the last decade the Netherlands has experienced an economic recession. Now, in 2017, the economy is picking up again. This growth does not only come with advantages because economic growth demands more from the transport system. Congestion is increasing again, the capacity of the train system is now insufficient during peak hours, and the world faces environmental challenges that are partly due to emissions caused by travellers. These negative effects worsen as travellers make rational choices, which could be undesirable from a system, or social welfare, perspective. For example, car drivers do often not choose public transport options, because it costs them more effort; however, if they choose public transport options, then the system improves since congestion and emissions will reduce. Or another example, if travellers choose to avoid peak hours, they might not arrive at their desired time, but then they do not contribute to peak hour congestion or crowding. In addition, the capacity of the transport system is more effectively used if travellers spread out over the day. Passenger transport pricing can be an incentive for travellers to change their choices, and can therefore be used to mitigate congestion, emissions, and other undesirable effects. Passenger transport pricing is the umbrella term for measures that make passengers pay for their travels. Traditional pricing measures are for example: fuel excise taxes, public transport fares, and periodical registration fees for vehicles. More innovative measures are cordon charges (e.g., in London, Stockholm, and Singapore), special tolling lanes, and peak avoidance projects. When such an innovative measure has different prices for times of the day, and for different locations (i.e., it is time- and space-differentiated), travellers’ choices related to route, mode and departure time can be influenced. By changing these choices, the overall performance of the transport system can improve. Travellers have differences regarding time valuation, preferred departure or arrival times, and car ownership. Therefore, a measure can become even more effective if it also allows to differentiate amongst characteristics of travellers. However, innovative pricing measures have not been implemented widely across the globe, despite their potential to reduce congestion and emissions. This is primarily due to lack of public and political support. The Netherlands has experienced decades of political discourse and many failed proposals. Low public support did not contribute to (political) agreement either, because it has always fuelled the discussion with dissenting opinions. In the process of designing policies and making decisions, strategic planning models usually estimate (or forecast) the effects of the policy. The preferences of travellers and the transport system are captured by mathematical equations. Such models are always a simplified representation of reality. To apply them to asses pricing measures, they should capture the underlying mechanisms that are important for transport pricing as realistic as possible. This dissertation identifies disadvantages of current strategic network models for passenger transport pricing and provides methodological advances to resolve them. This is done with a holistic approach that combines game theory, discrete choice analysis, traffic flow theory, and tranport economics into one modelling framework. This framework has many sub-models and provides a toolbox for analysts to determine the effects of innovative pricing schemes. The basic principle for each tool is to make them realistic (so that the results are credible for decision makers), and computationally efficient. The latter means that many different pricing schemes can be computed within reasonable time. By providing the methodological advances, that are briefly discussed in the next sections, this dissertation aims to improve public and political support. For example, the preferences of multiple stakeholders can be considered, the possible conflicts between them can be identified, and solutions based on concepts that aim to resolve these conflicts can be computed.

Incorporating an information acquisition process into a route choice model with multiple information sources