Travel analytics dashboard tracking 1,000 trips across 150 travelers and six transportation modes. The dataset spans 3.85 million kilometers of total distance with aggregate CO₂ emissions of 512,773 kg. Analysis includes temporal patterns, mode distributions, flight route networks, delay variance by airline, and activity heatmaps showing trip initiation patterns by weekday and hour.
Use template -> Travel patterns and mode distribution
The dataset captures multimodal transportation activity with detailed metrics for distance, duration, and timing.
Flight dominates the mode mix at 43% of trips. This accounts for 430 flights with an average arrival delay of 6.6 minutes. Train follows at 18.9%, then car at 17.5%, with bus (9.1%), bike (6.6%), and walk (4.9%) comprising the remaining share. The high flight proportion likely reflects the dataset's emphasis on longer-distance travel, given the average trip distance of 3,852 km.
Total distance traveled reaches 3.85 million kilometers. Average speed across all modes is 355.9 km/h, heavily influenced by the 43% flight share. This aggregate metric obscures significant variance between modes—flights travel at ~800-900 km/h while ground transportation ranges from 5 km/h (walking) to 100+ km/h (car/train). The distance vs duration scatter plot would reveal these mode-specific speed clusters and identify outliers such as unusually slow flights or implausibly fast ground trips.
Route frequency shows concentration on specific paths. The most common route is Cairo to Abu Dhabi (CAI → AUH) with 2 flights, though this relatively low maximum frequency suggests the dataset captures diverse routing rather than heavy repetition on trunk routes. The world flight map visualization encodes route frequency through line width, allowing identification of geographic travel patterns and hub airports.
Temporal distribution reveals usage patterns. The trips-over-time chart tracks daily trip counts to identify seasonality, weekday-weekend differences, and anomalous spikes. The activity heatmap crosses weekday with hour-of-day to show when trips typically begin—likely revealing morning peaks for business travel and different patterns for leisure trips. Total travel time reaches 7,091 hours across the 1,000 trips.
Performance metrics and environmental impact
Beyond basic travel statistics, the dashboard tracks operational performance and carbon footprint.
Flight delays show airline-level variance. Average arrival delay of 6.6 minutes masks variation between carriers. The delay-by-airline visualization displays the distribution of arrival delays, revealing whether some carriers consistently outperform others or if delays are uniformly distributed. This metric captures only arrival delays, not departure delays or total delay time, which may understate passenger-experienced delays.
CO₂ emissions aggregate to 512,773 kg across all trips. The emissions-by-mode breakdown shows contribution by transportation type. Despite representing 43% of trips, flights likely account for a disproportionate share of total emissions due to aviation's high carbon intensity per kilometer. Walking and biking contribute near-zero emissions, while car, bus, and train emissions depend on factors like vehicle efficiency, load factors, and energy sources that may be modeled in the underlying calculations.
Distance-duration relationships enable outlier detection. Plotting distance against duration for each mode creates distinct clusters—flights should follow a roughly linear relationship with higher speed, ground transportation clusters at lower speeds, and walking/biking at the lowest speeds. Points far from their mode's cluster might indicate data errors, unusual routing, or exceptional delays. Checking for outliers helps validate data quality before drawing conclusions.
The dataset structure supports segmentation by traveler. With 150 travelers generating 1,000 trips, average trip frequency is 6.7 trips per person, though the distribution may be skewed with some frequent travelers and others making single trips. Per-traveler metrics would reveal whether certain individuals disproportionately contribute to emissions or distance traveled, potentially informing targeted interventions for sustainability programs.