Valuation of uncertainty in travel time and arrival time - some findings from a choice experiment

We are developing a dynamic modeling framework in which we can evaluate the effects of different road pricing measures on individual choice behavior as well as on a network level. Important parts of this framework are different choice models which forecast the route, departure time and mode choice behavior of travelers under road pricing in the Netherlands. In this paper we discuss the setup of the experiment in detail and present our findings about dealing with uncertainty, travel time and schedule delays in the utility functions. To develop the desired choice models a stated choice experiment was conducted. In this experiment respondents were presented with four alternatives, which can be described as follows: Alternative A: paying for preferred travel conditions. Alternative B: adjust arrival time and pay less. Alternative C: adjust route and pay less. Alternative D: adjust mode to avoid paying charge. The four alternatives differ mainly in price, travel time, time of departure/arrival and mode and are based on the respondents’ current morning commute characteristics. The travel time in the experiment is based on the reported (by the respondent) free-flow travel time for the home-to-work trip, and the reported trip length. We calculate the level of travel time, by setting a certain part of the trip length to be in free-flow conditions and calculate a free-flow and congested part of travel time. Adding the free-flow travel time and the congested travel time makes the total minimum travel time for the trip. Minimum travel time, because to this travel time we add an uncertainty margin, creating the maximum travel time. The level of uncertainty we introduced between minimum and maximum travel time was based on the difference between the reported average and free-flow travel time. In simpler words then explained here, we told respondents that the actual travel time for this trip is unknown, but that between the minimum and maximum each travel time has an equal change of occurring. As a consequence of introducing uncertainty in travel time, the arrival time also receives the same margin. Using the data from the experiment we estimated choice models following the schedule delay framework from Vickrey (1969) and Small (1987), assigning penalties to shifts from the preferred time of departure/arrival to earlier or later times. In the models we used the minimum travel time and the expected travel time (average of minimum and maximum). Using the expected travel time incorporates already some of the uncertainty (half) in the attribute travel time, making the uncertainty attribute in the utility function not significant. The parameters values and values-of-time for using the minimum or expected travel time do not differ. Initially, we looked at schedule delays only from an arrival time perspective. Here we also distinguished between schedule delays based on the minimum arrival time and the expected arrival time (average of minimum and maximum). Again, when using expected schedule delays the uncertainty is included in the schedule delays and a separate uncertainty attribute in the utility function is not significant. There is another issue involved when looking at the preferred arrival time of the respondents; there are three cases to take into account: 1.If the minimum and maximum arrival times are both earlier than the preferred arrival time we are certain about a schedule delay early situation (based on minimum or expected schedule delays). 2.If the minimum and maximum arrival times are both later than the preferred arrival time we are certain about a schedule delay late situation (based on minimum or expected schedule delays). 3.The scheduling situation is undetermined when the preferred arrival time is between the minimum and maximum arrival time. In this case we use an expected schedule delay assuming a uniform distribution of arrival times between the minimum and maximum arrival time. Parameter values for both situations are very different and results from the minimum arrival time approach are more in line with expectations. There is a choice to take into account uncertainty in the utility function in either the expected travel time, expected schedule delays or as a separate attribute. In the paper we discuss the effects of different approaches. We extended our models to also include schedule delays based on preferred departure time. In the departure time scheduling components uncertainty is not included. Results show that the depart schedule delay late is significant and substantial, together with significant arrival schedule early and late. Further extension of the model includes taking into account the amount of flexibility in departure and arrival times for each respondent. The results will be included in this paper.

Valuation of uncertainty in travel time and arrival time - some findings from a choice experiment

We are developing a dynamic modeling framework in which we can evaluate the effects of different road pricing measures on individual choice behavior as well as on a network level. Important parts of this framework are different choice models which forecast the route, departure time and mode choice behavior of travelers under road pricing in the Netherlands. In this paper we discuss the setup of the experiment in detail and present our findings about dealing with uncertainty, travel time and schedule delays in the utility functions. To develop the desired choice models a stated choice experiment was conducted. In this experiment respondents were presented with four alternatives, which can be described as follows: Alternative A: paying for preferred travel conditions. Alternative B: adjust arrival time and pay less. Alternative C: adjust route and pay less. Alternative D: adjust mode to avoid paying charge. The four alternatives differ mainly in price, travel time, time of departure/arrival and mode and are based on the respondents' current morning commute characteristics. The travel time in the experiment is based on the reported (by the respondent) free-flow travel time for the home-to-work trip, and the reported trip length. We calculate the level of travel time, by setting a certain part of the trip length to be in free-flow conditions and calculate a free-flow and congested part of travel time. Adding the free-flow travel time and the congested travel time makes the total minimum travel time for the trip. Minimum travel time, because to this travel time we add an uncertainty margin, creating the maximum travel time. The level of uncertainty we introduced between minimum and maximum travel time was based on the difference between the reported average and free-flow travel time. In simpler words then explained here, we told respondents that the actual travel time for this trip is unknown, but that between the minimum and maximum each travel time has an equal change of occurring. As a consequence of introducing uncertainty in travel time, the arrival time also receives the same margin. Using the data from the experiment we estimated choice models following the schedule delay framework from Vickrey (1969) and Small (1987), assigning penalties to shifts from the preferred time of departure/arrival to earlier or later times. In the models we used the minimum travel time and the expected travel time (average of minimum and maximum). Using the expected travel time incorporates already some of the uncertainty (half) in the attribute travel time, making the uncertainty attribute in the utility function not significant. The parameters values and values-of-time for using the minimum or expected travel time do not differ. Initially, we looked at schedule delays only from an arrival time perspective. Here we also distinguished between schedule delays based on the minimum arrival time and the expected arrival time (average of minimum and maximum). Again, when using expected schedule delays the uncertainty is included in the schedule delays and a separate uncertainty attribute in the utility function is not significant. There is another issue involved when looking at the preferred arrival time of the respondents; there are three cases to take into account: 1.If the minimum and maximum arrival times are both earlier than the preferred arrival time we are certain about a schedule delay early situation (based on minimum or expected schedule delays). 2.If the minimum and maximum arrival times are both later than the preferred arrival time we are certain about a schedule delay late situation (based on minimum or expected schedule delays). 3.The scheduling situation is undetermined when the preferred arrival time is between the minimum and maximum arrival time. In this case we use an expected schedule delay assuming a uniform distribution of arrival times between the minimum and maximum arrival time. Parameter values for both situations are very different and results from the minimum arrival time approach are more in line with expectations. There is a choice to take into account uncertainty in the utility function in either the expected travel time, expected schedule delays or as a separate attribute. In the paper we discuss the effects of different approaches. We extended our models to also include schedule delays based on preferred departure time. In the departure time scheduling components uncertainty is not included. Results show that the depart schedule delay late is significant and substantial, together with significant arrival schedule early and late. Further extension of the model includes taking into account the amount of flexibility in departure and arrival times for each respondent. The results will be included in this paper

Rewarding instead of charging road users: a model case study investigating effects on traffic conditions

Instead of giving a negative incentive such as transport pricing, a positive incentive by rewarding travelers for ‘good behavior’ may yield different responses. In a Dutch pilot project called Peak Avoidance (in Dutch: “SpitsMijden”), a few hundred travelers participated in an experiment in which they received 3 to 7 euros per day when they avoided traveling by car during the morning rush hours (7h30–9h30). Mainly departure time shifts were observed, together with moderate mode shifts. Due to the low number of participants in the experiment, no impact on traffic conditions could be expected. In order to assess the potential of such a rewarding scheme on traffic conditions, a dynamic traffic assignment model has been developed to forecast network wide effects in the long term by assuming higher participation levels. This paper describes the mathematical model. Furthermore, the Peak Avoidance project is taken as a case study and different rewarding strategies with varying participation levels and reward levels are analyzed. First results show that indeed overall traffic conditions can be improved by giving a reward, where low to moderate reward levels and participation levels of 50% or lower are sufficient for a significant improvement. Higher participation and reward levels seem to become increasingly counter-effective

Optimising Differentiated Tolls on Large Scale Networks, by using an Intellegent Search Algorithm

The design of an optimal road pricing scheme is not a trivial problem. Following the Dutch government’s kilometre charge plans, this paper focuses on the optimisation of link based toll levels differentiated in space and time. The optimal toll level design problem is formulated as a bi-level mathematical program. In the upper level we minimise an object function, e.g. the average travel time in the network, using a fixed number of price categories. At the lower level a dynamic traffic assignment model is used to determine the effects of differentiated road pricing schemes on the traffic system. Focus of the paper is on the upper-level where optimal toll levels are approximated. In the optimisation procedure different variants of a pattern search algorithm are tested in a case study. Inspection of the solution space shows that many local minima exist, so the selection of the initial solution becomes important. In the case study however it appears that in all local minima the value of the objective function is almost the same, indicating the fact that many different toll schemes result in the same average travel time. The case study is also used to test the performance of the different variants of the pattern search algorithm. It appears that it is beneficial to change more variables at a time and to use a memory to remember where improvement of the objective function has been made. First tests on a medium scale network showed that it is possible to apply the framework on this network, though further computational improvements are needed to apply the framework to large scale networks, for example by parallel processing

Regulatory T cell defects in rheumatoid arthritis

No abstract.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/55983/1/22415_ftp.pd

Phenotypic, Functional, and Gene Expression Profiling of Peripheral CD45RA+ and CD45RO+ CD4+CD25+CD127^low Treg Cells in Patients with Chronic Rheumatoid Arthritis

OBJECTIVE: Conflicting evidence exists regarding the suppressive capacity of Treg cells in the peripheral blood (PB) of patients with rheumatoid arthritis (RA). The aim of this study was to determine whether Treg cells are intrinsically defective in RA. METHODS: Using a range of assays on PB samples from patients with chronic RA and healthy controls, CD3+CD4+CD25+CD127(low) Treg cells from the CD45RO+ or CD45RA+ T cell compartments were analyzed for phenotype, cytokine expression (ex vivo and after in vitro stimulation), suppression of Teff cell proliferation and cytokine production, suppression of monocyte-derived cytokine/chemokine production, and gene expression profiles. RESULTS: No differences between RA patients and healthy controls were observed with regard to the frequency of Treg cells, ex vivo phenotype (CD4, CD25, CD127, CD39, or CD161), or proinflammatory cytokine profile (interleukin-17 [IL-17], interferon-γ [IFNγ], or tumor necrosis factor [TNF]). FoxP3 expression was slightly increased in Treg cells from RA patients. The ability of Treg cells to suppress the proliferation of T cells or the production of cytokines (IFNγ or TNF) upon coculture with autologous CD45RO+ Teff cells and monocytes was not significantly different between RA patients and healthy controls. In PB samples from some RA patients, CD45RO+ Treg cells showed an impaired ability to suppress the production of certain cytokines/chemokines (IL-1β, IL-1 receptor antagonist, IL-7, CCL3, or CCL4) by autologous lipopolysaccharide-activated monocytes. However, this was not observed in all patients, and other cytokines/chemokines (TNF, IL-6, IL-8, IL-12, IL-15, or CCL5) were generally suppressed. Finally, gene expression profiling of CD45RA+ or CD45RO+ Treg cells from the PB revealed no statistically significant differences between RA patients and healthy controls. CONCLUSION: Our findings indicate that there is no global defect in either CD45RO+ or CD45RA+ Treg cells in the PB of patients with chronic RA

Association of the IL2RA/CD25 Gene With Juvenile Idiopathic Arthritis

OBJECTIVE: IL2RA/CD25, the gene for interleukin-2 receptor alpha, is emerging as a general susceptibility gene for autoimmune diseases because of its role in the development and function of regulatory T cells and the association of single-nucleotide polymorphisms (SNPs) within this gene with type 1 diabetes mellitus (DM), Graves' disease, rheumatoid arthritis (RA), and multiple sclerosis (MS). The aim of this study was to determine whether SNPs within the IL2RA/CD25 gene are associated with juvenile idiopathic arthritis (JIA). METHODS: Three SNPs within the IL2RA/CD25 gene, that previously showed evidence of an association with either RA, MS, or type 1 DM, were selected for genotyping in UK JIA cases (n=654) and controls (n=3,849). Data for 1 SNP (rs2104286) were also available from North American JIA cases (n=747) and controls (n=1,161). Association analyses were performed using Plink software. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated. RESULTS: SNP rs2104286 within the IL2RA/CD25 gene was significantly associated with UK JIA cases (OR for the allele 0.76 [95% CI 0.66-0.88], P for trend=0.0002). A second SNP (rs41295061) also showed modest evidence for association with JIA (OR 0.80 [95% CI 0.63-1.0], P=0.05). Association with rs2104286 was convincingly replicated in the North American JIA cohort (OR 0.84 [95% CI 0.65-0.99], P for trend=0.05). Meta-analysis of the 2 cohorts yielded highly significant evidence of association with JIA (OR 0.76 [95% CI 0.62-0.88], P=4.9x10(-5)). CONCLUSION: These results provide strong evidence that the IL2RA/CD25 gene represents a JIA susceptibility locus. Further investigation of the gene using both genetic and functional approaches is now required