The aim of this paper is to extend past research on travel behavior analysis by investigating travelers’ emotions and perceptions of the system’s performance. Perceived travel happiness as an extension of travel satisfaction is researched in the framework of the decision-making process during traveling. Socio-demographic, cognitive, and affective data were collected from a questionnaire survey that took place in Athens (Greece), the Netherlands, and Barcelona and Salamanca (Spain). A Bayesian network was developed to investigate the interrelations between travel happiness and parameters that affect travel behavior. Findings revealed that travel mode choice directly affects the level of happiness that a person experiences during everyday trips. Moreover, travel happiness is directly associated with the traveler’s perception of the occurrence of disruptions during everyday trips and the level of tolerance he/she has toward such disruptions. Results also indicated that further research should focus on understanding how the topology and performance of each country’s system affect travel-related choices. Finally, a discussion of the most significant results is provided.

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Sags are roadway sections along which the gradient increases gradually in the direction of traffic. Sags are generally bottlenecks in freeway networks. Previous research suggests that traffic management measures using advanced Adaptive Cruise Control (ACC) systems could reduce congestion on freeways, but little is known about their potential effectiveness at sags. This article evaluates the effectiveness of a basic ACC system (B-ACC) and two advanced ACC systems – Traffic State-Adaptive ACC (TSA-ACC) and Cooperative ACC (C-ACC) – in mitigating congestion at sags. TSA-ACC adapts the ACC parameters to the macroscopic traffic state estimated by the vehicle itself. C-ACC uses information of other vehicles in the surroundings to adjust its accelerations. Results are obtained using microscopic traffic simulations with different penetration rates. They show that, under high-demand conditions, congestion decreases with increasing percentage of vehicles equipped with B-ACC. With high penetration rates (75% and above), traffic no longer becomes congested at the sag. Moreover, the results show that TSA-ACC and C-ACC reduce congestion more than B-ACC, mainly because they increase the queue discharge capacity of the sag. The two advanced ACC systems prevent the formation of congestion at the sag at lower penetration rates than B-ACC. TSA-ACC is the most effective system. C-ACC is only more effective than B-ACC in scenarios with 20% penetration rate or higher; below that, connectivity between equipped vehicles is too low. Our findings show the potential of using advanced ACC systems to mitigate congestion at sags and indicate some challenges of this traffic management approach.@en