Bottleneck Model

Daily mobility patterns in highly populated urban environments rely on a well-functioning effective road network. Nevertheless, traffic bottlenecks are typical for urban environments with periodic traffic congestion. In this paper, we focus on the investigation of how traffic congestion is related with complicated crossings. First, we select an approach for the classification of the complexity of road partitions and the derivation of complicated crossings based on geodata from Open-StreetMap (OSM). Second, we calculate traffic congestions using Floating Taxi Data (FTD) from Shanghai in 2007. Then, we develop a matching technique to link the congestion and complicated crossings, and subsequently define the concept of traffic bottlenecks represented by polygons. The bottlenecks indicate locations where the transportation infrastructure is complex and traffic congestion appears periodically. Finally, we select suitable cartographic representations of traffic bottlenecks in potential thematic vehicle traffic maps.

This paper considers coarse tolling of congestion under heterogeneous preferences, and especially the distributional effects of such tolls. With coarse tolling, the toll equals a fixed value during the centre of the peak; outside this period, it is zero. This paper investigates three dimensions of heterogeneity. With the first, all values of time and schedule delay vary in fixed proportions, and this heterogeneity may stem from income differences. The second has differences in flexibility of users when to arrive. The third captures differences in willingness to arrive before or after the preferred arrival time. The paper uses three models of coarse tolling: the Laih, ADL, and Braking model.
All three dimensions affect the welfare gain of coarse tolling. In the Laih model, the generalised price with coarse tolling is in between the no-toll and first-best one. In the other models this is not so, and distributional effects may be non-monotonic and very different from the first-best toll’s effects. In the Braking model, the capacity goes unused for some time during the tolled period. Compared with in the Laih model, this raises total cost, and it is most harmful for users with low values and difficulty to arrive late: e.g. low-income users with a strict work start time or a trip to the doctor.

When analysing the effects of transport policies it is important to adequately control for heterogeneity: previous studies note that ignoring heterogeneity biases the estimated welfare effects of tolling. This paper examines the effects of tolling, in a bottleneck model, with a continuously distributed value of time. With homogeneous users, first-best public tolling has no effect on prices. With heterogeneity it does: low values of time lose, and high values of time gain. The average congestion externality decreases with the heterogeneity in the value of time. Consequently, the welfare gain of first-best tolling also decreases. The more heterogeneous the value of time is, the lower the relative efficiency of a public pay-lane. This finding contrasts with the previous literature. Earlier studies, using static flow congestion, conclude that the relative efficiency increases with this type of heterogeneity. With more heterogeneity in the value of time, the relative efficiency of a private pay-lane is also lower, while that of a public time-invariant toll is higher. Our results suggest that the welfare gains of different tolling schemes are affected differently by heterogeneity. Further, the impact of heterogeneity on the effects of a policy also depends on the type of congestion considered.

This paper analyses the efficiency and distributional impacts of congestion pricing in Vickrey’s (1969) dynamic bottleneck model of congestion, allowing for continuous distributions of values of time and schedule delay. We find that congestion pricing can leave a majority of travellers better off even without returning the toll revenues to them. We also find that the consumer surplus losses or gains from tolling are not strictly monotonic in the value of time, because they also depend on the value of schedule delays. The greatest losses are not incurred by drivers with the lowest value of time, but by users with an intermediate value of schedule delays and the lowest value of time for that value of schedule delays. For second-best pricing with an untolled alternative, the pattern of distributional effects is quite similar to that for first-best pricing. In contrast with results from prior static models, users who are indifferent between the two alternative routes are not the ones who gain least from this type of second-best pricing. Our results suggest that, in assessing the distributional impacts of road congestion pricing, it is important to take into account both the distribution of the value of time and of the value of schedule delays, as well as the dynamics of departure time choice.